Biomarkers for dengue

ABSTRACT

The present invention provides protein-based biomarkers and biomarker combinations that are useful in qualifying dengue status in a patient. In particular, the biomarkers of this invention are useful to classify a subject sample as infected with dengue or not infected with dengue. The biomarkers can be detected by SELDI mass spectrometry.

FIELD

The invention relates generally to clinical diagnostics and prognostics for infection.

BACKGROUND

“Break-bone fever”, or dengue fever (DF), was first spread worldwide in the tropics during the 18^(th) and 19^(th) century following the expansion of the commerce and shipping industry. The Aedes aegypti, main mosquito vector, was introduced, along with the dengue virus (DENV), in the new regions chartered by the industry. During the last decade, dengue was able to spread due to an increase in air travel, unprecedented population growth, unplanned and uncontrolled urbanization, and the lack of mosquito control among other things (Rigau-Perez, J., et al., 1998, Lancet 352:971-977). Today it is estimated that 2.5 billion people are at risk of DENV infection in more than 100 countries in the Americas, Southeast Asia, western Pacific, Africa and the eastern Mediterranean. There is an estimated 50 million cases of dengue infection each year with 500,000 cases of dengue hemorrhagic fever (the more severe case of the disease) and at least 12,000 deaths, mostly in children (DengueNet, 2002, Weekly Epidemiological Record 77:300-304).

Dengue virus belongs to the Flavivirus genus that also includes yellow fever, West Nile, tick-borne encephalitis (TBEV), and Japanese encephalitis viruses. There are 4 primary serotypes that exist which can cause different degrees of disease severity ranging from the mildest form of dengue fever (DF), to dengue hemorrhagic fever (DHF), and the most severe form of dengue shock syndrome (DSS). DENV possesses an icosahedral core of 40-50 nm in diameter, containing one of the 3 structural proteins, the C protein. It encapsulates the 10,700 nucleotide plus-sense RNA genome. Surrounding the core is a smooth lipid bilayer composed of the other 2 structural proteins, the membrane (M) protein, and the envelope glycoprotein (E) (Kuhn, R. J., et al., 2002, Cell 108:717-725). The main biological properties of the virus come from the E protein where it allows for receptor binding, haemagglutination of erythrocytes, neutralizing antibody induction, and protective immune response (Chang, G. J. 1997, p. 175-198. In D. J. Gubler and G. Kuno (ed.), CAB International, New York). It also possesses 7 non-structural proteins (NS1, NS2a, NS2b, NS3, NS4a, NS4b, NS5), of which two, NS1 and NS3, are believed to be the most important ones involved in the pathogenesis. Upon primary infection with DENV, antibodies against the surface E, NS1, and NS3 proteins are generated (Green, S. and A. Rothman, 2006, Current Opinion in Infectious Diseases 19:429-436.). Therefore serotypes can be distinguished by virus-neutralizing antibodies, but non-neutralizing antibodies against the E protein and non-structural proteins NS1 and NS3 are cross-reactive. A life-long immunity against the infective serotype ensues, but protection against others is only for a short period of time. During a second infection by a different serotype, the presence of neutralizing antibodies can reduce the severity of the disease. However, if the levels of these antibodies drop under the neutralizing amount, the heterotypic IgG antibodies form complexes with dengue viruses that can bind to the FcyR resulting in an augmentation of the virus infection. This model is called the antibody dependent enhancement (ADE) (Green, S. and A. Rothman, 2006, Current Opinion in Infectious Diseases 19:429-436; Guzman, M. G. and G. Kouri. 2002, The Lancet Infectious Diseases 2:33-42; Kliks, S. C., et al., 1989, American Journal of Tropical Medicine & Hygiene 40:444-451; Oishi, K., et al., 2003, Journal of Medical Virology 71:259-264; and Stephenson, J. R., 2005, Bulletin of the World Health Organization 83:308-314). To further support this model, it has been observed that the incidence of DHF/DSS in children occurs at two distinct peaks in their lives. The first occurs when the child is 6-9 months old. This is the age at which the maternal antibodies are still present in the circulation. If the child gets infected by a different heterotypic DENV than the mother, DHF/DSS ensues since the levels of maternal antibodies have fallen below the protective levels (Simmons, C. P., et al., Journal of Infectious Diseases 196:416-424). The other peak occurs in young children infected for a second time. ADE supports the fact that DHF/DSS is 15-80 times more likely in secondary infections. However, this can not explain the whole pathogenesis of dengue virus and many other factors still to be studied might play a role such as the strain's virulence and the serotype, and the host susceptibility and the specific role of T cells (Chaturvedi, U., et al., 2006, FEMS Immunology & Medical Microbiology 47:155-166, Fink, J., et al., 2006, Reviews in Medical Virology 16:263-275). All these factors need to be considered in the design of a vaccine (Stephenson, J. R., 2005, Bulletin of the World Health Organization 83:308-314).

Once one is bitten by an infected mosquito, there is an incubation period of up to 2 weeks. Most infections are asymptomatic, especially in children under 15 years of age, but can cause a range of symptoms and even lead to death. Population-based studies have shown that the severity of the disease increases with the patient's age (Burke, D. S., 1988, American Journal of Tropical Medicine & Hygiene 38:172-80, Cobra, C., et al., 1995, American Journal of Epidemiology 142:1204-1211, Dietz, V., et al., 1996. Puerto Rico Health Sciences Journal 15:201-210; and Kuberski, T., et al., 1977, American Journal of Tropical Medicine & Hygiene 26:775-783). DF is an acute febrile disease often characterized by frontal headache, retroocular pain, muscle and joint pain, nausea, vomiting, and rash (Kalayanarooj, S., et al., 1997, Journal of Infectious Diseases 176:313-321). The febrile period usually terminates between 5-7 days after the onset of symptoms, often correlating with the disappearance of the virus from the circulation. In Southeast Asia, DHF is mostly seen in children, but it is seen in all age groups in the tropical Americas. This suggests the involvement of race or strain virulence as risk factors. DHF is an acute febrile illness, typically with bleeding, thrombocytopenia, elevated haematocrit, pleural effusions, and hypoproteinaemia. It begins as DF with a sudden onset of fever, and then develops into DHF around 3-7 days of illness (around the time of defervescence for DF) and continues for about 2-7 days. The main pathophysiological difference between DF and DHF is plasma leakage. Dengue shock syndrome (DSS) is the most severe form of the disease characterized by circulatory failure and a narrowing pulse range. Once shock begins, the fatality rate can be as high as 44% if the proper precautions are not taken (Oishi, K., et al., 2003, Journal of Medical Virology 71:259-264). There are no antiviral drugs administered nor are any drugs known to be useful in limiting the plasma leakage. Dengue treatment is only supportive where analgesics and antipyretics (but not aspirin) are given and fluid management is applied. Only when the molecular biology of DHF is understood will we able to treat it (Lei, H. Y., et al., 2001, Journal of Biomedical Science 8:377-388; and Rigau-Perez, J., et al., 1998, Lancet 352:971-977). This is why the diagnostic of a dengue infection needs to be given early in the disease progression so to maximize the patient's chance of survival. However, clinical findings alone are often not very helpful in distinguishing DF from other febrile illnesses (OFIs) such as the chikungunya, measles, leptospirosis, yellow fever, influenza, West Nile, Japanese, and St Louis encephalitis (Rigau-Perez, J., et al., 1998, Lancet 352:971-977; Senanayake, S., 2006, Australian Family Physician 35:609-612; and Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho, 2005, Reviews in Medical Virology 15:287-302).

During a primary infection, IgM antibodies are developed after 5-6 days and are present in the circulation for up to 2-3 months after infection, while IgG antibodies become present after only 7-10 days. On the other hand, a secondary infection occurs when an individual has been previously infected or immunized with a flavivirus. IgM levels are lower if not absent but IgG levels are very high, even during the acute phase of the infection. Therefore, IgM is a sign of an early infection while high levels of IgG reveal a secondary infection (Guzman, M. G. and G. Kouri, 2002, The Lancet Infectious Diseases 2:33-42). Viable DENV particles are detectable in the circulation for up to 5 days after the symptoms but then rapidly disappear upon the appearance of DENV-specific antibodies (Kao, C. L., et al., 2005, Journal of Microbiology, Immunology & Infection 38:5-16).

Enzyme immunoassay (EIA) is used to detect IgM and IgG antibodies to dengue. This method can distinguish a primary infection from a secondary infection by determining the IgM/IgG ratio; if the ratio in convalescent sera exceeds 1.5, it reveals a primary infection. The World Health Organization (WHO) recommends the use of the dengue monoclonal antibody (IgM)-capture EIA (MAC-EIA) which is inexpensive, simple, fast, and only requires one blood sample. However, IgM antibodies can only be detected at least 5 days after infection since this is the time needed for the body to produce anti-dengue antibodies. Moreover, some false-positives can occur due to the persistence of IgM in the blood even after a few months (Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho, 2005, Reviews in Medical Virology 15:287-302).

The haemagglutination-inhibition (HI) is slightly more sensitive than the EIA test. On the other hand, chemical treatment of the samples is needed to remove non-specific inhibitor of heamagglutination as well as non-specific agglutinins Moreover, this test does not differentiate between closely related flavivirus infections or different DENV serotypes. Paired sera are needed and so the results can take weeks.

There exists also the neutralization test which is more sensitive than the HI-test but employs live virus and so Biosafety Level 3 Laboratories are needed. It also encounters the same difficulties as the HI-test in terms of specificity in addition to the extra cost, time, and technical difficulty associated with the neutralizing test (Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho, 2005, Reviews in Medical Virology 15:287-302).

The complement fixation (CF) test is a good marker of recent infection compared to the detection of IgM dengue specific antibodies due to their short persistence in the blood. However, the CF antibody appears only 7-14 days after the onset of symptoms. Also, it is the least sensitive of the serological tests.

Due to some cross-reactivity in flaviviruses, any serologic test must include as controls the four dengue serotypes, another serotype, a non-flavivirus and an uninfected control for it to be a confirmatory diagnosis (Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho, 2005, Reviews in Medical Virology 15:287-302). Also, the high rate of IgG positive results for people in the tropics indicate that paired acute and convalescent serum samples are often critical for the significance of the tests (Rigau-Perez, J., et al., 1998, Lancet 352:971-977).

Inoculation of clinical specimens into mosquito cells, larvae or adult mosquitoes is the most sensitive approach. Specific detection and identification of the virus by immunofluorescence assays with serotype-specific anti-dengue monoclonal antibodies makes this technique able to determine the serotype of DENV. This test is convenient since the samples are relatively suitable for 2 weeks and the test does not require special facilities or special training (Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho, 2005, Reviews in Medical Virology 15:287-302). However, days to weeks are necessary for virus isolation and the cost of equipment and laboratory maintenance is high (Kao, C. L., et al., 2005, Journal of Microbiology, Immunology & Infection 38:5-16).

RNA viral genome can be detected by PCR-based techniques, e.g., RT-PCR. It is a technique that is just as expensive as the virus culture technique with higher contamination risks associated with sample manipulation, but only takes a few hours to perform and is much more sensitive. By using 4 serotype-specific oligonucleotide primers, it is also possible to detect the serotype of the given DENV (Teles, F. R., D. M. Prazeres, and J. L. Lima-Filho, 2005, Reviews in Medical Virology 15:287-302).

Thus a need exists for the identification of biomarkers that could simplify the diagnosis and/or prognosis of dengue and its symptoms at, e.g., reduced costs. The present invention provides for these and other advantages, as described below.

SUMMARY

The present invention provides, inter alia, biomarkers that are differentially present in subjects with dengue. In addition, the present invention provides methods of using the biomarkers to qualify dengue in a subject or in a biological sample taken from a subject, including a sample of serum, blood, or other donated tissue. As such, the invention provides biomarkers that represent full length proteins or fragments of proteins expressed in infected individuals by a member of the Flaviviridae family, the pathogen responsible for dengue.

The biomarkers can be used, inter alia, to qualify dengue status, determine the course of dengue, monitor the response to treatment by a drug used to treat dengue, and/or determine a treatment regimen for dengue. The dengue can be caused by members of the Flaviviridae family.

In one aspect, the present invention provides a method for qualifying dengue status in a subject, the method including: (a) measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers of Tables 1-5, 17, 21, and 24; and (b) correlating the measurement with dengue status. In one aspect, the biological sample is a serum sample.

The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 2.5, 2.6, 2.7, 2.8, 3.0, 3.2, 3.4, 3.5, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 5.0, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.0, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.4, 7.5, 7.6, 8.2, 8.8, 9.0, 9.3, 9.5, 9.6, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.3, 11.5, 11.7, 11.8, 11.9, 12.1, 12.2, 12.4, 12.5, 12.6, 12.7, 12.9, 13.0, 13.1, 13.2, 13.3, 13.4, 13.5, 13.8, 14.0, 14.1, 14.2, 14.3, 14.4, 15.3, 17.4, 17.7, 17.8, 18.0, 21.5, 22.4, 23.1, 23.3, 23.6, 23.7, 23.8, 25.4, 25.6, 26.5, 26.7, 28.2, 28.4, 29.0, 30.3, 30.8, 31.2, 32.3, 33.5, 33.6, 34.0, 34.2, 34.5, 34.7, 36.5, 36.6, 38.7, 39.5, 39.9, 42.3, 43.5, 44.0, 44.6, 44.7, 45.0, 45.4, 45.5, 45.6, 46.2, 46.6, 46.7, 49.7, 50.5, 51.7, 52.4, 52.6, 53.4, 53.6, 54.3, 54.4, 54.6, 54.8, 55.1, 55.3, 55.8, 56.6, 59.4, 59.5, 61.4, 63.1, 66.6, 66.7, 66.8, 67.1, 69.0, 70.9, 71.3, 71.5, 75.1, 75.2, 75.3, 77.1, 79.1, 79.3, 88.3, 95.7, 109.0, 111.3, 117.2, 123.0, 125.4, 133.4, 133.7, 150.1, 164.6, 188.6, 194.2, and 198.3 kDa and any combination thereof.

The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 2.5, 2.6, 2.7, 2.8, 3.0, 3.2, 3.4, 3.5, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 5.0, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.0, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.4, 7.5, 7.6, 8.2, 8.8, 9.0, 9.3, 9.5, 9.6, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.3, 11.5, 11.7, 11.8, 11.9, 12.1, 12.2, 12.4, 12.5, 12.6, 12.7, 12.9, 13.0, 13.1, 13.2, 13.3, 13.4, 13.5, 13.8, 14.0, 14.1, 14.2, 14.3, 14.4, 15.3, 17.4, 17.7, 17.8, 18.0, 21.5, 22.4, 23.1, 23.3, 23.6, 23.7, 23.8, and 25.4 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 25.6, 26.5, 26.7, 28.2, 28.4, 29.0, 30.3, 30.8, 31.2, 32.3, 33.5, 33.6, 34.0, 34.2, 34.5, 34.7, 36.5, 36.6, 38.7, 39.5, 39.9, 42.3, 43.5, 44.0, 44.6, 44.7, 45.0, 45.4, 45.5, 45.6, 46.2, 46.6, 46.7, 49.7, 50.5, 51.7, 52.4, 52.6, 53.4, 53.6, 54.3, 54.4, 54.6, 54.8, 55.1, 55.3, 55.8, 56.6, 59.4, 59.5, 61.4, 63.1, 66.6, 66.7, 66.8, 67.1, 69.0, 70.9, 71.3, 71.5, 75.1, 75.2, 75.3, 77.1, 79.1, 79.3, 88.3, 95.7, 109.0, 111.3, 117.2, 123.0, 125.4, 133.4, 133.7, 150.1, 164.6, 188.6, 194.2, and 198.3 kDa and any combination thereof.

The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 3.4, 3.8, 3.9, 4.0, 4.6, 6.6, 6.7, 7.0, 7.6, 10.6, 11.1, 11.7, 12.5, 12.7, 12.9, 13.1, 13.2, 13.3, 13.4, 14.4, 23.1, 23.3, 23.6, 23.8, 25.4, 34.2, 44.7, 45.6, 46.2, 46.4, 56.6, 117.2, 133.4, 133.7, 198.3 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 3.4, 3.8, 3.9, 4.0, 4.6, 6.6, 6.7, 7.0, 7.6, 10.6, 11.1, 11.7, and 12.5 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 12.7, 12.9, 13.1, 13.2, 13.3, 13.4, 14.4, 23.1, and 23.3 kDa. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 23.6, 23.8, 25.4, 34.2, and 44.7 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 45.6, 46.2, 46.4, 56.6, 117.2, 133.4, 133.7, and 198.3 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 3.4, 3.8, 3.9, 4.0, 4.6, 6.6, 6.7, 133.4, 133.7, and 198.3 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 3.4, 3.8, 3.9, 25.4, 34.2, 44.7, 45.6, 46.2, and 46.4 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 4.6, 25.4, 34.2, and 44.7 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 7.0, 7.6, 10.6, 11.1, 11.7, 12.5, 12.7, 12.9, 13.1, 13.2, 13.3, 13.4, 14.4, 23.8, 25.4, 34.2, 44.7, and 45.6 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 3.4, 3.8, 3.9, 4.0, 4.6, 6.6, 6.7, 117.2, 133.4, 133.7, and 198.3 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 12.7, 12.9, 13.1, 13.2, and 13.3 kDa and any combination thereof.

The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 2.6, 2.7, 2.8, 3.5, 4.2, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.7, 6.8, 6.9, 7.0, 7.4, 8.8, 9.0, 9.3, 9.5, 9.6, 10.3, 10.9, 11.3, 11.5, 11.7, 11.8, 11.9, 12.4, 12.6, 12.9, 13.5, 13.8, 25.6, 32.3, 39.9, 42.3, 44.0, 44.6, 45.0, 46.6, 46.7, 49.7, 53.6, 54.4, 55.8, 63.1, 67.1, 75.3, 88.3, 111.3, and 150.1 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 2.6, 2.7, 2.8, 3.5, 4.2, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.7, and 6.8 kDa and any combination thereof. The at least one biomarker can be selected from the group consisting of biomarkers of molecular masses of about 6.9, 7.0, 7.4, 8.8, 9.0, 9.3, 9.5, 9.6, 10.3, 10.9, 11.3, 11.5, 11.7, 11.8, 11.9, and 12.4 kDa and any combination thereof. It will be understood that any combination of the biomarkers described herein can be measured using the methods described herein.

In some aspects, the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 12.6, 12.9, 13.5, 13.8, 25.6, 32.3, 39.9, 42.3, 44.0, 44.6, and 45.0 kDa and any combination thereof. In some aspects, the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 46.6, 46.7, 49.7, 53.6, 54.4, 55.8, 63.1, 67.1, 75.3, 88.3, 111.3, and 150.1 kDa and any combination thereof. In some aspects, the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 2.6, 2.7, 2.8, 3.5, 4.2, 5.1, 5.2, 5.3, 67.1, 75.3, 88.3, 111.3, and 150.1 kDa and any combination thereof. In some aspects, each of the biomarkers having a molecular mass of about 75.3, 88.3, 111.3, and 150.1 kDa is measured.

In some aspects, the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 2.6, 2.7, 11.7, 11.8, 11.9, 12.4, 67.1, 75.3, 88.3, 111.3, and 150.1 kDa and any combination thereof. In some aspects, the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 7.4, 8.8, 9.0, 9.3, 9.5, 9.6, 10.3, 10.9, 12.4, 12.6, 12.9, 13.5, 13.8, 25.6, and 32.3 kDa and any combination thereof. In some aspects, the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 11.5, 25.6, and 32.3 kDa and any combination thereof.

In some aspects, the at least one biomarker is a protein or fragment thereof as provided in Table 5. In certain aspects, the at least one biomarker is represented by at least one of the accession numbers provided in Table 5.

In one aspect, the at least one biomarker is measured by capturing the biomarker on an adsorbent of a SELDI probe and detecting the captured biomarkers by laser desorption-ionization mass spectrometry. In certain aspects, the adsorbent is a cation exchange adsorbent, whereas in other aspects, the adsorbent is a metal chelation adsorbent. In another aspect, the at least one biomarker is measured by immunoassay.

In another aspect, the correlating is performed by a software classification algorithm. In a further aspect, dengue status is selected from chronically infected versus uninfected. In yet other aspects, dengue status is selected from chronically infected status versus acutely infected disease status, chronically infected asymptomatic status versus chronically affected with symptoms, or acutely infected status versus healthy uninfected status. In still another aspect, dengue status is selected from dengue versus healthy. In yet other aspects, dengue status is selected from dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). In other aspects, the biomarkers of the present invention can be used to predict the effectiveness of a dengue vaccine. In other aspects, dengue status is selected from primary infection and secondary infection.

In yet another aspect, the method further comprises managing subject treatment based on the status. If the measurement correlates with dengue, then managing subject treatment comprises administering to a patient drugs selected from a group consisting of, but not necessarily limited to, drugs such as paracetamol, antipyretics, and combinations thereof.

In a further aspect, the method further comprises measuring the at least one biomarker after subject management.

In another aspect, the present invention provides a method comprising measuring at least one biomarker in a sample from a subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers set forth in Tables 1-5, 17, 21, and 24. In one aspect, the sample is a serum sample.

In still another aspect, the present invention provides a kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds at least one biomarker from a first group consisting of the biomarkers set forth in Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24; and (b) instructions for using the solid support to detect the at least one biomarker set forth in Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24.

In other aspects, the kit additionally comprises (c) a container containing at least one of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24.

In yet a further aspect, the present invention provides a software product, the software product comprising: (a) code that accesses data attributed to a sample, the data comprising measurement of at least one biomarker in the sample, the biomarker selected from the group consisting of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24; and (b) code that executes a classification algorithm that classifies dengue status of the sample as a function of the measurement.

In one aspect, the classification algorithm classifies dengue status of the sample as a function of the measurement of a biomarker selected from the biomarkers of Tables 1-5, 17, 21, and 24.

In other aspects, the present invention provides purified biomolecules selected from the biomarkers set forth in Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24 and, additionally, methods comprising detecting a biomarker set forth in Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24 by mass spectrometry or immunoassay.

In yet another aspect, the method further comprises testing and qualifying stocks of blood based on the status of blood which has been tested according to the methods described herein. If the measurements taken from blood samples correlate with dengue, then the management of blood stocks comprises decontamination of the infected blood by treatment of the infected blood with purification agents available to one skilled in the art. Alternatively, the infected blood can be discarded or destroyed and only stocks of blood which have not tested positively for dengue are retained.

In one aspect, the present invention provides a method for qualifying dengue status in a subject in comparison to the status of a different viral infection, the method comprising: (a) measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker specifically indicates the presence of dengue and does not indicate the presence of a different infection; and (b) correlating the measurement with dengue status in comparison to the status of a different infection. In one aspect, the biological sample is a serum sample. In a preferred aspect of this method, the at least one biomarker is selected from the group of biomarkers of Tables 1-5, 17, 21, and 24. In still another preferred aspect, the infection includes, but is not limited to other febrile illnesses (OFIs).

In another aspect, the present invention provides a method for monitoring the course of progression of dengue in a patient comprising: (a) measuring at least one biomarker in a first biological sample from the patient, wherein the at least one biomarker specifically indicates the presence of dengue; (b) measuring the at least one biomarker in a second biological sample from the subject, wherein the second biological sample was obtained from the subject after the first biological sample; and (c) correlating the measurements with the progression or regression of dengue in the subject. In one aspect, the at least one biomarker is selected from the group consisting of the biomarkers of Tables 1-5, 17, 21, and 24.

Other features, objects and advantages of the invention and its preferred aspects will become apparent from the detailed description, examples and claims that follow.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, and accompanying drawings, where:

FIG. 1 shows Biomarker Pattern Software analysis results for fraction F1CSL (fraction 1 using CM10 at low laser intensity). Using the indicated splitters, 100.000% sensitivity and 94.737% specificity was achieved.

FIG. 2 shows a graphical representation from CiphergenExpress of 3 candidate dengue diagnostic biomarkers of the F1CSL fraction. (A) Predicted MW of 4580 Da. (B) Predicted MW of 3957 Da. (C) Predicted MW of 3870 Da. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 3 shows a graphical representation from CiphergenExpress of a candidate dengue diagnostic biomarker with predicted MW of 4292 Da in the FISL fraction. (A) CE graphical representation of control and DHF at t2. (B) CE graphical representation of control and DF at t2. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 4 shows Biomarker Pattern Software analysis results for fraction FlISH (fraction 1 using IMAC at high laser intensity). Using the indicated splitters, 92.592% sensitivity and 100.000% specificity was achieved.

FIG. 5 shows a graphical representation from CiphergenExpress of a candidate dengue diagnostic biomarker with predicted MW of 23105 Da in the F1ISH fraction. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 6 shows Biomarker Pattern Software analysis results for fraction F5CSH (fraction 5 using CM10 at high laser intensity). Using the indicated splitters, 100.000% sensitivity and 100.000% specificity was achieved.

FIG. 7 shows a graphical representation from CiphergenExpress 2 candidate dengue diagnostic biomarker with predicted MW of 4292 Da in the FISL fraction. (A) CE graphical representation of control and DHF at t1 of candidate biomarker with predicted MW of 12919 Da. (B) CE graphical representation of control and DHF at t2 of candidate biomarker with predicted MW of 13092 Da. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 8 shows a graphical representation from CiphergenExpress of a candidate dengue diagnostic biomarker with predicted MW of 12650 Da in the F6CSL fraction. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 9 shows a graphical representation from CiphergenExpress of a candidate dengue diagnostic biomarker with predicted MW of 3437 Da in the F61SL fraction. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 10 shows Biomarker Pattern Software analysis results for fraction F6ISH (fraction 6 using IMAC at high laser intensity). Using the indicated splitters, 93.333% sensitivity and 100.00% specificity was achieved.

FIG. 11 shows a graphical representation from CiphergenExpress of a candidate dengue diagnostic biomarker with predicted MW of 13317 Da in the F6ISH fraction. Each dot represents a sample. The relative intensity of each protein is represented on the y-axis.

FIG. 12 shows a 4-12% Bis-Tris NuPAGE Gel #1 of pooled controls (C) at time 1 and 2 compared to pooled dengue samples of DF and DHF at time 1 and 2 (D). ZOOM Fractionated and desalted (200 μl). Lane 1 and 10, Marker 12 MW (invitrogen). Each C or D sample was desalted and ZOOM Fractionated using specific pI ranges corresponding to the pH indicated on the figure. The boxes indicate the potential diagnostic biomarkers sent for sequencing.

FIG. 13 shows a graphical representation of the differential signal intensity of the AMBP protein precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 14 shows a graphical representation of the differential signal intensity of the Apolipoprotein A-I precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 15 shows a graphical representation of the differential signal intensity of the Apolipoprotein D precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 16 shows a graphical representation of the differential signal intensity of the C4b-binding protein a chain precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 17 shows a graphical representation of the differential signal intensity of the Carboxypeptidase N subunit 2 precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 18 shows a graphical representation of the differential signal intensity of the Ceruloplasmin precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 19 shows a graphical representation of the differential signal intensity of the Complement Clq subcomponent subunit B precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 20 shows a graphical representation of the differential signal intensity of the Hemoglobin subunit a biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 21 shows a graphical representation of the differential signal intensity of the Hemopexin precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 22 shows a graphical representation of the differential signal intensity of the Insulin-like growth factor-binding protein complex acid labile chain biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 23 shows a graphical representation of the differential signal intensity of the Plasma protease Cl inhibitor precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 24 shows a graphical representation of the differential signal intensity of the Sertransferrin precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 25 shows a graphical representation of the differential signal intensity of the Vitamin K-dependent protein S precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 26 shows a graphical representation of the differential signal intensity of the Vitronectin precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 27 shows a graphical representation of the differential signal intensity of the alpha1B-glycoprotein precursor biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 28 shows a graphical representation of the differential signal intensity of the 3806 and 4596 DA biomarkers in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 29 shows a graphical representation of the differential signal intensity of the 23,260 DA biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 30 shows a graphical representation of the differential signal intensity of the 12,662 DA biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 31 shows a graphical representation of the differential signal intensity of the 13,295 DA biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 32 shows a graphical representation of the differential signal intensity of the 12,650 DA biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 33 shows a graphical representation of the differential signal intensity of the 7,625 DA biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

FIG. 34 shows a graphical representation of the differential signal intensity of the 13,317 DA biomarker in control, DF, and DHF groups. The biomarker is characterized by its mass-to-charge ratio as determined by mass spectrometry and is represented in daltons.

DETAILED DESCRIPTION Introduction

A biomarker is an organic biomolecule which is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease) as compared with another phenotypic status (e.g., not having the disease). A biomarker is differentially present between different phenotypic statuses if the expression level of the biomarker (e.g., as indicated by the mean, median, or other measure) in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and odds ratio. Biomarkers, alone or in combination, provide measures of relative risk that a subject belongs to one phenotypic status or another. Therefore, they are useful as markers for disease (diagnostics), therapeutic effectiveness of a drug (theranostics), drug toxicity, and the like.

It is to be understood that this invention is not limited to particular methods, reagents, compounds, compositions, or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular aspects only, and is not intended to be limiting. As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a biomarker” includes a combination of two or more biomarkers, and the like.

“About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20% or ±10%, more preferably ±5%, even more preferably ±1%, and still more preferably ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although any methods and materials similar or equivalent to those described herein can be used in the practice for testing of the present invention, the preferred materials and methods are described herein. In describing and claiming the present invention, the following terminology will be used.

The term “in situ” refers to processes that occur in a living cell growing separate from a living organism, e.g., growing in tissue culture.

The term “in vivo” refers to processes that occur in a living organism.

The term “mammal” as used herein includes both humans and non-humans and include but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines.

As used herein, the term “residue” refers to amino acids or analogs thereof.

As used herein, the term “peptide” refers to peptides, proteins, fragments of proteins, peptidomimetics, and the like that are comprised of more than one amino acid residue or similar molecule.

The term percent “identity,” in the context of two or more nucleic acid or polypeptide sequences, refer to two or more sequences or subsequences that have a specified percentage of nucleotides or amino acid residues that are the same, when compared and aligned for maximum correspondence, as measured using one of the sequence comparison algorithms described below (e.g., BLASTP and BLASTN or other algorithms available to persons of skill) or by visual inspection. Depending on the application, the percent “identity” can exist over a region of the sequence being compared, e.g., over a functional domain, or, alternatively, exist over the full length of the two sequences to be compared.

For sequence comparison, typically one sequence acts as a reference sequence to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. The sequence comparison algorithm then calculates the percent sequence identity for the test sequence(s) relative to the reference sequence, based on the designated program parameters.

Optimal alignment of sequences for comparison can be conducted, e.g., by the local homology algorithm of Smith & Waterman, 1981, Adv. Appl. Math. 2:482, by the homology alignment algorithm of Needleman & Wunsch, 1970, J. Mol. Biol. 48:443, by the search for similarity method of Pearson & Lipman, 1988, Proc. Nat'l. Acad. Sci. USA 85:2444, by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr., Madison, Wis.), or by visual inspection (see generally Ausubel et al., infra).

One example of an algorithm that is suitable for determining percent sequence identity and sequence similarity is the BLAST algorithm, which is described in Altschul et al., 1990, J. Mol. Biol. 215:403-410. Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information (www.ncbi.nlm.nih.gov/).

The term “sufficient amount” means an amount sufficient to produce a desired effect, e.g., an amount sufficient to modulate protein aggregation in a cell.

The term “therapeutically effective amount” is an amount that is effective to ameliorate a symptom of a disease. A therapeutically effective amount can be a “prophylactically effective amount” as prophylaxis can be considered therapy.

A biomarker is an organic biomolecule which is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease) as compared with another phenotypic status (e.g., not having the disease). A biomarker is differentially present between different phenotypic statuses if the mean or median expression level of the biomarker in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and odds ratio. Biomarkers, alone or in combination, provide measures of relative risk that a subject belongs to one phenotypic status or another. Therefore, they are useful as markers for disease (diagnostics), therapeutic effectiveness of a drug (theranostics), prognostics, and drug toxicity.

The term “chronic” refers to a disease or condition that is long-lasting or recurrent. The term chronic describes the course of the disease, or its rate of onset and development. A chronic course is distinguished from a recurrent course; recurrent diseases or conditions relapse repeatedly, with periods of remission in between.

The term “acute” means an exacerbated event or attack, of short course, followed by a period of remission.

Biomarkers for Dengue

This invention provides, inter alia, polypeptide-based biomarkers that are differentially present in subjects having dengue, in particular, and particularly that are differentially expressed in subjects infected with dengue versus non uninfected individuals (e.g., control, healthy, benign condition or other disease state). The biomarkers are characterized by mass-to-charge ratio as determined by mass spectrometry, by the shape of their spectral peak in time-of-flight mass spectrometry and by their binding characteristics to adsorbent surfaces. These characteristics provide one method to determine whether a particular detected biomolecule is a biomarker of this invention. These characteristics represent inherent characteristics of the biomolecules and not process limitations in the manner in which the biomolecules are discriminated. In one aspect, this invention provides these biomarkers in isolated form.

The biomarkers of Tables 3-4 were discovered using SELDI technology employing ProteinChip® arrays from Ciphergen Biosystems, Inc. (Fremont, Calif.) (“Ciphergen”). Serum samples were collected from subjects diagnosed with dengue and subjects diagnosed as healthy as well as subjects diagnosed with other febrile illnesses (OFIs). “Other febrile illnesses” are defined as cases with no evidence of dengue infection and no obvious bacterial, rickettsial or protozoan etiology, including, without limitation, chikungunya, measles, leptospirosis, yellow fever, influenza, West Nile, Japanese, and St Louis encephalitis. The samples were fractionated by anion exchange chromatography. Fractionated samples were applied to SELDI biochips and spectra of polypeptides in the samples were generated by time-of-flight mass spectrometry on a Ciphergen PBS IIc mass spectrometer. The spectra thus obtained were analyzed by Ciphergen Express™ Data Manager Software with Biomarker Wizard and Biomarker Pattern Software from Ciphergen Biosystems, Inc. The mass spectra for each group were subjected to scatter plot analysis. A Mann-Whitney test analysis was employed to compare dengue and control groups for each protein cluster in the scatter plot, and proteins were selected that differed significantly (p<0.05) between the two groups. This method is described in more gel electrophoresis followed by protein identification by matrix-assisted laser desorption/ionization mass spectrometry (DIGE and MALDI-TOFMS). This method is described in more detail in the Examples.

The biomarkers thus discovered are presented in Tables 1-4 (the protocol for the data obtained is further described below in the Examples).

TABLE 1 Biomarkers identified using differential SDS-PAGE gel followed by protein identification by matrix-assisted laser desorption/ionization mass spectroscopy. Samples were fractionated using ZOOM IEF Fractionator (Invitrogen). Approximate Predicted position on gel molecular Calculated Fraction Band # (kDa) ID weight (Da) pI value F1 (pH 2.3  116.3-200.00 α2-macroglobulin 164600 6.00 3.0-4.6) 4.5 66.3-97.4 plasma protease C1 55347 6.09 inhibitor precursor carboxypeptidase N 61431 5.63 subunit 2 precursor α1-acid glycoprotein 23725 4.93 1 precursor serotransferrin 79280 6.81 precursor lumican precursor 38747 6.16 6.7 55.4-66.3 lumican precursor 38747 6.16 hemopexin 52385 6.55 precursor 8.9 31.0-36.5 apolipoprotein D 21547 5.06 precursor 10.11   10-14.4 complement C4 A 194247 6.65 precursor F2 (pH 12.13  116.3-200.00 α2-macroglobulin 164600 6.00 4.6-5.4) AMBP protein 39886 5.95 precursor 14.15  97.4-116.3 ceruloplasmin 122983 5.44 precursor apolipoprotein A-I 30759 5.56 precursor complement C4 A 194247 6.65 precursor plasma protease C1 55347 6.09 inhibitor precursor hemopexin 52385 6.55 precursor apolipoprotein B-100 516666 6.61 16.17 66.3-97.4 prothrombin 71475 5.64 precursor lumican precursor 38747 6.16 insulin-like growth 66735 6.33 factor-binding protein complex acid labile chain precursor α1B-glycoprotein 54809 5.58 precursor apolipoprotein A-I 30759 5.56 precursor afamin precursor 70963 5.64 haptoglobin-related 39496 6.42 protein precursor vitamin K-dependent 77127 5.48 protein S precursor complement C4 A 194247 6.65 precursor apolipoprotein A-IV 45371 5.28 precursor hemopexin 52385 6.55 precursor vitronectin precursor 55069 5.55 18.19 14.4-21.5 apolipoprotein A-I 30759 5.56 precursor haptoglobin-related 39496 6.42 protein precursor F3 (pH 20.21 31.0-36.5 mannose binding 26526 5.39 5.4-7.0) protein C precursor complement C4 A 194247 6.65 precursor prothrombin 71475 5.64 precursor haptoglobin-related 39496 6.42 protein precursor fibrinogen α chain 95656 5.70 precursor complement C3 188569 6.02 precursor F4 (pH 22.23 ~66.3 complement C3 188569 6.02 7.0-9.1) precursor complement C4 A 194247 6.65 precursor serotransferrin 79280 6.81 precursor fibrinogen α chain 95656 5.70 precursor C4b-binding protein 69042 α chain precursor 24.25 ~36.5 complement C1q 26670 8.83 subcomponent subunit B precursor serotransferrin 79280 6.81 precursor complement C4 A 194247 6.65 precursor prothrombin 71475 5.64 precursor complement C3 188569 6.02 precursor fibrinogen α chain 95656 5.70 precursor 26.27 14.4-21.5 haptoglobin-related 39496 6.42 protein precursor complement 22435 8.49 component C8 γ chain precursor F5 (pH 28.29  116.3-200.00 9.1- 10.0) 30.31 ~116.3 tRNA(Ile)-lysine 50497 9.57 synthase- Streptococcus mutans 32.33 55.4-66.3 complement C3 188569 6.02 precursor complement C4 A 194247 6.65 precursor 34.35 36.5-55.4

TABLE 2 TABLE 2. Biomarkers identified employing differential SDS-PAGE gel followed by matrix-assisted laser desorption/ionization mass spectroscopy, their presence/absence in sample and their obtained scores using MASCOTT search engine. Approximate Predicted position on molecular Calculated Coverage Fraction Band # gel (kDa) ID weight (Da) pI value (%) Ct DV Score F1 (pH 2.3  116.3-200.00 α2-macroglobulin 164600 6.00 4 x 390 3.0-4.6) 4.5 66.3-97.4 plasma protease C1 55347 6.09 11 x 311 inhibitor precursor carboxypeptidase N 61431 5.63 9 x xx 220 subunit 2 precursor α1-acid glycoprotein 23725 4.93 3 x 52 1 precursor serotransferrin 79280 6.81 3 x 99 precursor lumican precursor 38747 6.16 5 x 60 6.7 55.4-66.3 Serum albumin 71317 5.92 32 x xx 1236 precursor lumican precursor 38747 6.16 7 x 130 hemopexin 52385 6.55 4 x 73 precursor 8.9 31.0-36.5 apolipoprotein D 21547 5.06 28 xx x 229 precursor 10.11   10-14.4 complement C4 A 194247 6.65 0 x 49 precursor F2 (pH 12.13  116.3-200.00 α2-macroglobulin 164600 6.00 1 x 63 4.6-5.4) AMBP protein 39886 5.95 3 x 54 precursor 14.15  97.4-116.3 Serum albumin 71317 5.92 12 x 396 precursor ceruloplasmin 122983 5.44 12 x xx 529 precursor apolipoprotein A-I 30759 5.56 12 x 112 precursor complement C4 A 194247 6.65 1 x 78 precursor plasma protease C1 55347 6.09 4 x 65 inhibitor precursor hemopexin 52385 6.55 2 x 49 precursor apolipoprotein B- 516666 6.61 0 x 45 100 16.17 66.3-97.4 prothrombin 71475 5.64 42 x xx 1187 precursor lumican precursor 38747 6.16 14 xx x 225 insulin-like growth 66735 6.33 9 x xx 299 factor-binding protein complex acid labile chain precursor α1B-glycoprotein 54809 5.58 11 xx x 187 precursor apolipoprotein A-I 30759 5.56 12 xx x 154 precursor afamin precursor 70963 5.64 10 xx x 154 9% cov. scor 219 haptoglobin-related 39496 6.42 6 x 99 protein precursor vitamin K- 77127 5.48 5 x xx 184 dependent protein S precursor complement C4 A 194247 6.65 0 x 67 precursor apolipoprotein A-IV 45371 5.28 2 x 49 precursor hemopexin 52385 6.55 4 x x 95 precursor vitronectin precursor 55069 5.55 3 x 92 18.19 14.4-21.5 apolipoprotein A-I 30759 5.56 19 xx x 213 precursor haptoglobin-related 39496 6.42 3 x xx 137 protein precursor F3 20.21 31.0-36.5 mannose binding 26526 5.39 26 x xx 292 (pH 5.4-7.0) protein C precursor complement C4 A 194247 6.65 4 x x 277 precursor prothrombin 71475 5.64 5 x 154 precursor haptoglobin-related 39496 6.42 3 x 62 protein precursor fibrinogen α chain 95656 5.70 2 x 73 precursor Serum albumin 71317 5.92 23 x xx 715 precursor complement C3 188569 6.02 0 x 46 precursor F4 24.25  ~36.5 complement C1q 26670 8.83 19 x xx 216 (pH 7.0-9.1) subcomponent subunit B precursor Ig gamma-1 chain C 36596 8.46 32 x xx 426 region Serum albumin 71317 5.92 6 x 206 precursor serotransferrin 79280 6.81 4 x 120 precursor complement C4 A 194247 6.65 5 x xx 319 precursor Ig gamma-2 chain C 36489 7.66 11 x xx 146 region Ig gamma-4 chain C 36431 7.18 13 x 94 region prothrombin 71475 5.64 1 x 90 precursor complement C3 188569 6.02 3 x xx 268 precursor fibrinogen α chain 95656 5.70 10 x 314 precursor 26.27 14.4-21.5 haptoglobin-related 39496 6.42 3 xx x 127 protein precursor Serum albumin 71317 5.92 8 xx x 228 precursor Hemoglobin subunit 15305 8.72 25 x 221 alpha Ig gamma-1 chain C 36596 8.46 10 x xx 136 region complement 22435 8.49 5 xx x 63 component C8 γ chain precursor F5 28.29  116.3-200.00 Ig gamma-1 chain C 36596 8.46 3 x 47 (pH 9.1-10.0) region 30.31 ~116.3 Ig gamma-1 chain C 36596 8.46 8 xx x 110 region Ig gamma-2 chain C 36489 7.66 5 x 64 region tRNA(Ile)-lysine 50497 9.57 3 x 46 synthase- Streptococcus mutans 32.33 55.4-66.3 complement C3 188569 6.02 4 x 266 precursor Ig gamma-1 chain C 36596 8.46 19 xx x 268 region Ig gamma-2 chain C 36489 7.66 12 x 167 region complement C4 A 194247 6.65 1 x 111 precursor 34.35 36.5-55.4 Ig gamma-1 chain C 36596 8.46 3 x 59 region

TABLE 3 Table 3. Predicted correlation between biomarkers discovered employing differential SDS-PAGE gel followed by protein identification by matrix-assisted laser desorption/ionization mass spectroscopy and those employing SELDI technology. TABLE 3 Proposed Proteins Gel SELDI Sample found M/Z Average Protein Mass Ct DFNV Fraction Ct1_2 DF1_2 DHF1_2

70963 x x x x precursor AMBE protein 39686 x x F6ISH 39686.176 ± 8.381 39887.686 ± 8.490 39891.827 ± 7.9 precursor Apolipoprotein

x x x x Acl precursor Apolipoprotein 46371 x x F6ISH

± 19.421

± 22.022 46363.08 ± 0.08049

F6CSH 45682.198 ± 4.882 45581.119 ± 9.163 0.08639 ± 0.02933 precursor Apolipoprotein 616668 x x x B-100 Apolipoprotein 21547 xx x F6CSL 33613.916 ± 3.735 88614.675 ± 8.733 88863.863 ± 4.863 D precursor

) F6ISL 33656.728 ± 0.81 33556.081 ± 1.271 33558.493 ± 0.836 C4b-binding 69042 x x F1CSH 89023.681 ± 7.865 89821.468 ± 9.752 89023.41 ± 10.408 protein α F6CSL 34624.834 ± 0.893 34623.590 ± 4.66 34821.235 ± 7.390 chain precursor Carboxypeptidase 61431 x xx F6CSH 61383.104 ± 3.227 61384.759 ± 5.492 61.383.023 ± 4.071 N subunit 2 precursor

122963 x xx F6CSH 108960.906 ± 12.625 108981.316 ± 8.176 108960.41 ± 2.362 precursor Complement 26670 x xx F6CSH 25404.284 ± 6.504 25403.649 ± 4.573 26404.844 ± 4.573 C1q subcomponent

& precursor Complement 188669 x x x C2 precursor Complement 194247 x x x x C4A precursor Complement 62435 xx x x component C3

chain precursor Fibrinogen α 95856 x x x x chain precursor Haptoglobin- 39496 xx x x x related protein precursor Hemoglobin 16385 x x F1CSH 16308.449 ± 0.683 16308.648 ± 0.829 16308.689 ± 1.241 subunit alpha

62365 x xx F6ISH 52579.804 ± 14.203 62678.178 ± 13.289 52686.671 ± 7.276 precursor Ic mu heavy 43543 x x x x chain disease protein Ic gamma-1 38686 x x x x chain C region Ic gamma-2 38489 x x x x chain C region Insulin-like 66735 x xx F8CSH 66724.441 ± 0.384 66724.559 ± 0.338 68724.523 ± 8.481 growth factor- F6ISH 66626.217 ± 0.43 66626.311 ± 0.478 66626.319 ± 0.550 binding, protein, complex acid labile chain, precursor Lumican 387.57 x x x x crocuses Mammas, 25526 x xx x binding, protein C, precursor Plasma 55347 x x F1ISH 55237.604 ± 23.135 55287.389 ± 33.414 55305.740 ± 28.403 protease C1 inhibitor precursor Prothrombin 71470 x xx x x precursor Serotransferrin 79280 x x F0CSH 79341.09 ± 4.442 79342.512 ± 4.3002 79341.031 ± 2.621 precursor Serum, 71317 x x x x Albumin, precursor IRNA

ab 50497 x x x x lysine sy

base, Stropto

matura Vitamin K1 77127 x xx F5CSH 75141.288 ± 2.803 75142.892 ± 2.12 75141.517 ± 1.906 da

dent, (75123) protein S, precursor V

actin, 55669 x x F6CSH 53489.215 ± 11.828 53486.247 ± 3.714 63466.088 ± 8.800 precursor α1-acid, 23725 x x x x phy

protein 1, precursor α18- 54809 xx x F1ISH 51693.736 ± 27.672 54594.675 ± 29.019 54692 ± 956 ± 26.418 pty

protein precursor α

- 184600 x x x x macroglobulin SELDI DHF1_DHF2 vs Intensity average Average DF1_DF2 Protein Ct1_2 DF1_2 DHF1_2 M/Z p value roc

x x x precursor AMBE protein 0.02268 ± 0.00357 0.03806 ± 0.02312 0.04406 ± 0.01453 39668.6485

0.3478861 precursor Apolipoprotein x x x Acl precursor Apolipoprotein 0.17419 ± 0.05317 0.23873 ± 0.09065 45368.9521 45368.9621 0.02369367 0.31481481

0.06414 ±

0.06085 ± 0.01474 45581.8839 45581.6839 1.0000000 0.5142045 precursor Apolipoprotein x x x B-100 Apolipoprotein 0.40412 ± 0.15261 0.23087 ± 0.1193 0.30821 ± 0.14392 33614.1787 0.35620899 0.80897438 D precursor 0.58585 ± 0.0234 0.41558 ± 0.0689 0.50485 ± 0.11008 33665.3982 0.2779327 0.8028431 C4b-binding 0.13767 ± 0.14204 0.09796 ± 0.04115 0.12274 ± 0.11603 60023.5419 0.9024018 0.4717282 protein α 0.41100 ± 0.16538 0.07664 ± 0.1223 0.32514 ± 0.15886 34523.5514 0.37303721 0.59466128 chain precursor Carboxypeptidase 0.10387 ± 0.03894 0.0672 ± 0.2669 0.10007 ± 0.02767 61383.3748 0.0021063 0.2073864 N subunit 2 precursor

0.0628 ± 0.01843 0.07162 ± 0.02190 0.08466 ± 0.02747 108961.408 0.0614734 0.3258999 precursor Complement 0.07889 ± 0.08123 0.13804 ± 0.05838 0.13699 ± 0.04529 25404.0365 0.6574155 0.4886384 C1q subcomponent

& precursor Complement x x x C2 precursor Complement x x x C4A precursor Complement x x x component C3

chain precursor Fibrinogen α x x x chain precursor Haptoglobin- x x x related protein precursor Hemoglobin 0.72968 ± 0.42315 0.68053 ± 0.42315 0.41601 ± 0.22400 15308.5557 0.0805598 0.6517857 subunit alpha

0.02969 ± 0.01659 0.038181 ± 0.01165 0.05034 ± 0.01527 89680.3719 0.0108684 0.2376812 precursor Ic mu heavy x x x chain disease protein Ic gamma-1 x x x chain C region Ic gamma-2 x x x chain C region Insulin-like 2.80614 ± 0.68767 1.62726 ± 0.45856 1.70258 ± 0.41922 66794.6829 0.676938.7 0.4546466 growth factor- 1.91881 ± 0.43148 1.45151 ± 0.36184 1.79486 ± 0.42081 66626.9708 0.0190614 0.2724638 binding, protein, complex acid labile chain, precursor Lumican x x x crocuses Mammas, x x x binding, protein C, precursor Plasma 0.15307 ± 0.07217 0.03087 ± 0.03844 0.09185 ± 0.03664 55291.4169 0.53341644 0.43333333 protease C1 inhibitor precursor Prothrombin x x x precursor Serotransferrin 0.05046 ± 0.02730 0.06479 ± 0.03165 0.09141 ± 0.04297 29341.4807 0.0363029 0.3465908 precursor Serum, x x x Albumin, precursor IRNA

ab x x x lysine sy

base, Stropto

matura Vitamin K1 0.87103 ± 0.23150 0.86353 ± 0.29625 0.76484 ± 0.22220 75141.8895 0.1684346 0.6175595 da

dent, protein S, precursor V

actin, 0.0265 ± 0.01133 0.0339 ± 0.01367 0.04679 ± 0.01447 63487.7965 0.00860

40 0.227

722 precursor α1-acid, x x x phy

protein 1, precursor α18- 0.21745 ± 0.09393 0.11499 ± 0.95443 0.12176 ± 0.04866 54593.1773 0.6909161 0.45833333 pty

protein precursor α

- x x x macroglobulin SELDI Ct1_2 vs DHF1_2 Ct1_2 vs DF1_2 Protein p value roc p value roc

x x x x precursor AMBE protein 0.0000889 0.8400000 0.0019049 0.7946377 precursor Apolipoprotein x x x x Acl precursor Apolipoprotein 0.2187700 0.8

0.272779 0.4194444

0.0000113 0.0892857 0.0000728 0.1916584 precursor Apolipoprotein x x x x B-100 Apolipoprotein 0.0317443 0.3078451 0.0042800 0.2208333 D precursor 0.0163588 0.2819473 0.0331955 0.3333333 C4b-binding 0.6084078 0.4419848 0.3632777 0.4396269 protein α 0.1211833 0.3960784 0.0014444 0.2347222 chain precursor Carboxypeptidase 0.6962703 0.4732143 0.0081005 0.1916684 N subunit 2 precursor

0.0129359 0.7112500 0.0722886 0.6809624 precursor Complement 0.0001048 0.8268928 0.0000222 0.8409091 C1q subcomponent

& precursor Complement x x x x C2 precursor Complement x x x x C4A precursor Complement x x x x component C3

chain precursor Fibrinogen α x x x x chain precursor Haptoglobin- x x x x related protein precursor Hemoglobin 0.0097068 0.2867143 0.1824001 0.3937075 subunit alpha

0.0006866 0.8000000 0.0504608 0.8427538 precursor Ic mu heavy x x x x chain disease protein Ic gamma-1 x x x x chain C region Ic gamma-2 x x x x chain C region Insulin-like 0.0078297 0.2500000 0.0008317 0.2482687 growth factor- 0.4130817 0.4400000 0.0002888 0.2181359 binding, protein, complex acid labile chain, precursor Lumican x x x x crocuses Mammas, x x x x binding, protein C, precursor Plasma 0.0088733 0.3314815 0.0004176 0.2129830 protease C1 inhibitor precursor Prothrombin x x x x precursor Serotransferrin 0.0013931 0.7723214 0.1090298 0.6298701 precursor Serum, x x x x Albumin, precursor IRNA

ab x x x x lysine sy

base, Stropto

matura Vitamin K1 0.1727823 0.5300080 0.0073757 0.7295238 da

dent, protein S, precursor V

actin, 0.0000609 0.8437500 0.1323513 0.62398701 precursor α1-acid, x x x x phy

protein 1, precursor α18- 0.0009107 0.2098788 0.0001174 0.1629830 pty

protein precursor α

- x x x x macroglobulin

indicates data missing or illegible when filed

TABLE 4 Table 4. Summary list of most significant biomarkers discovered using SELDI technology. TABLE 4 DHF1_DHF2 vs DF1_DF2 Ct1_2 vs DHF1_2 Ct1_2 vs DF1_2 Intensity average Index p-value roc p-value roc p-value roc Ct1_2 F1CSL 20 0.0858999 0.3333333 0.0000002 0.9637815 0.0000000 0.9472789 0.13754 ± 0.09945 40 0.4194702 0.5714286 0.0000000 0.9915966 0.0000000 0.9778912 0.20533 ± 0.13663 F1CSH 42 0.0868537 0.3258929 0.0000003 0.9842857 0.0000448 0.8248299 1.01776 ± 0.3444982 F5CSL 23 0.7526763 0.4915459 0.0000023 0.1071429 0.0000006 0.1894720 0.26052 ± 0.18249 F5CSH 68 0.6677795 0.4717262 0.0000002 0.0200000 0.0000000 0.0342867 1.25365 ± 0.73605 F6CSL 106 0.091766 0.3333333 0.0000019 0.0960734 0.0000000 0.0444444 0.33588 ± 0.23484 F6ISL 22 0.0006400 0.8149510 0.4876235 0.4361339 0.0025296 0.7205480 0.40871 ± 0.22847 F6ISH 67 0.5806161 0.6913043 0.0000002 0.0100000 0.0000000 0.0297101 0.75444 ± 0.52943 Intensity average m/z, Index DF1_2 DHF1_2 average F1CSL 20 0.79791 ± 0.40975 1.14175 ± 0.61843 3808.262 40  4.5165 ± 3.52856 3.33219 ± 2.33453 4596.111 F1CSH 42 1.70585 ± 0.55744 2.07821 ± 0.42565 23260.27 F5CSL 23 0.04893 ± 0.02439  0.0486 ± 0.03122 12662.53 F5CSH 68 0.26347 ± 0.17809  0.2595 ± 0.13859 13295.38 F6CSL 106 0.04337 ± 0.02361 0.06224 ± 0.03383 12650.52 F6ISL 22  0.7085 ± 0.30474 0.39536 ± 0.32736 7605.507 F6ISH 67 0.12406 ± 0.0447  0.11899 ± 0.04494 13312.42 The biomarkers are characterized by their mass-to-charge ratio as determined by mass spectrometry. The mass-to-charge ratios were determined from mass spectra generated on a Ciphergen Biosystems, Inc. PBS IIc mass spectrometer. This instrument has a mass accuracy of about +/−0.15 percent. Additionally, the instrument has a mass resolution of about 400 to 1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height. The mass-to-charge ratio of the biomarkers was determined using Biomarker Wizard™ software (Ciphergen Biosystems, Inc.). Biomarker Wizard assigns a mass-to-charge ratio to a biomarker by clustering the mass-to-charge ratios of the same peaks from all the spectra analyzed, as determined by the PBSIIc, taking the maximum and minimum mass-to-charge-ratio in the cluster, and dividing by two. Accordingly, the masses provided reflect these specifications.

The identity of certain of the biomarkers of Tables 1-4 of this invention has been determined and is indicated in Tables 1-4 and/or Table 5. Table 5 shows the accession numbers for the biomarkers as determined on the NCBI web-site on Oct. 10, 2008. Thus, one of ordinary skill in the art could ascertain the nucleotide and amino acid sequences of the biomarkers based on this information without undue experimentation.

Tables 17-24 (below) show biomarkers of the invention. Table 17 shows the exemplary biomarkers for detecting primary DENV infection as detected by Biomarker Pattern Software (BPS). Tables B-D show all biomarkers detected by SELDI for primary DENV infection that had a p-value smaller than or equal to 0.05. Table 21 shows the exemplary biomarkers for detecting secondary DENV infection as detected by BPS. Tables F and G show the biomarkers for detecting secondary DENV infection. Table 24 shows the biomarkers that can be used to differentiate between primary and secondary DENV infection as detected by BPS.

For biomarkers whose identify has been determined, the presence of the biomarker can be determined by methods known in the art other than mass spectrometry.

TABLE 5 by alphabetical order TABLE 5. Non-redundant list of the discovered biomarkers using differential SDS-PAGE gel followed by protein identification by matrix-assisted laser desorption/ionization mass spectroscopy. Accession numbers as determined on the NCBI website on Oct. 10^(th), 2008. Predicted molecular Calculated Accession ID weight (Da) pI value number 1 afamin precursor 70963 5.64 NP_001124 2 AMBP protein 39886 5.95 P02760 precursor 3 apolipoprotein A-I 30759 5.56 NP_000030 precursor 4 apolipoprotein A-IV 45371 5.28 NP_000473 precursor 5 apolipoprotein B-100 516666 6.61 P04114 6 apolipoprotein D 21547 5.06 P05090 precursor 7 C4b-binding protein α 69042 P04003 chain precursor 8 carboxypeptidase N 61431 5.63 P22792 subunit 2 precursor 9 ceruloplasmin 122983 5.44 NP_000087 precursor 10 complement C1q 26670 8.83 P02746 subcomponent subunit B precursor 11 complement C3 188569 6.02 P01024 precursor 12 complement C4 A 194247 6.65 P0C0L4 precursor 13 complement 22435 8.49 P07360 component C8 γ chain precursor 14 fibrinogen α chain 95656 5.70 P02671 precursor 15 haptoglobin-related 39496 6.42 Q28801 protein precursor 16 Hemoglobin subunit 15305 8.72 P69905 alpha 17 hemopexin precursor 52385 6.55 AAA52704 18 Ig mu heavy chain 43543 5.13 P04220 disease protein 19 Ig gamma-1 chain C 36596 8.46 P20759 region 20 Ig gamma-2 chain C 36489 7.66 P01859 region 21 insulin-like growth 66735 6.33 P35858 factor-binding protein complex acid labile chain precursor 22 lumican precursor 38747 6.16 NP_002336 23 mannose binding 26526 5.39 P08661 protein C precursor 24 plasma protease C1 55347 6.09 AAB59387 inhibitor precursor 25 prothrombin precursor 71475 5.64 P00734 26 serotransferrin 79280 6.81 P02787 precursor 27 Serum albumin 71317 5.92 P02768 precursor 28 tRNA(Ile)-lysine 50497 9.57 Q8DWM9 synthase- Streptococcus mutans 29 vitamin K-dependent 77127 5.48 P07225 protein S precursor 30 vitronectin precursor 55069 5.55 NP_000629 31 α1-acid glycoprotein 1 23725 4.93 AAA40699 precursor 32 α1B-glycoprotein 54809 5.58 Q9EPH1 precursor 33 α2-macroglobulin 164600 6.00 CAA48670 34 LEAP-2 precursor NP_443203 35 LEAP-2 CAC51515

The biomarkers of this invention can be further characterized by the shape of their spectral peak in time-of-flight mass spectrometry.

The biomarkers of this invention can be further characterized by their binding properties on chromatographic surfaces.

Because the biomarkers are characterized by mass-to-charge ratio and binding properties, they can be detected by mass spectrometry without knowing their specific identity. The identity of certain of the biomarkers of Tables 1-4, and 17-24 is known and, if known, is shown in Tables 1-4 and/or Table 5. If desired, biomarkers whose identity is not determined can be identified by, for example, determining the amino acid sequence of the polypeptides. For example, a biomarker can be peptide-mapped with a number of enzymes, such as trypsin or V8 protease, and the molecular weights of the digestion fragments can be used to search databases for sequences that match the molecular weights of the digestion fragments generated by the various enzymes. Alternatively, protein biomarkers can be sequenced using tandem MS technology. In this method, the protein is isolated by, for example, gel electrophoresis. A band containing the biomarker is cut out and the protein is subject to protease digestion. Individual protein fragments are separated by a first mass spectrometer. The fragment is then subjected to collision-induced cooling, which fragments the peptide and produces a polypeptide ladder. A polypeptide ladder is then analyzed by the second mass spectrometer of the tandem MS. The difference in masses of the members of the polypeptide ladder identifies the amino acids in the sequence. An entire protein can be sequenced this way, or a sequence fragment can be subjected to database mining to find identity candidates.

The preferred biological source for detection of the biomarkers is serum. However, in other aspects, the biomarkers are detected in urine and other biological samples.

The biomarkers of this invention are biomolecules. Accordingly, this invention provides these biomolecules in isolated form. The biomarkers can be isolated from biological fluids, such as serum. They can be isolated by any method known in the art, based on both their mass and their binding characteristics. For example, a sample comprising the biomolecules can be subject to chromatographic fractionation, as described herein, and subject to further separation by, e.g., acrylamide gel electrophoresis. Knowledge of the identity of the biomarker also allows their isolation by immunoaffinity chromatography.

Biomarkers and Modified Forms of a Protein

Proteins frequently exist in a sample in a plurality of different forms. These forms can result from either, or both, of pre- and post-translational modification. Pre-translational modified forms include allelic variants, slice variants and RNA editing forms. Post-translationally modified forms include forms resulting from proteolytic cleavage (e.g., fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cysteinylation, sulphonation and acetylation. When detecting or measuring a protein in a sample, the ability to differentiate between different forms of a protein depends upon the nature of the difference and the method used to detect or measure. For example, immunological methods of detection typically cannot distinguish between different forms of a protein that contain the same epitope or epitopes to which the antibody or antibodies are directed. In diagnostic assays, the inability to distinguish different forms of a protein has little impact when the forms detected by the particular method used are equally good biomarkers as any particular form. However, when a particular form (or a subset of particular forms) of a protein is a better biomarker than the collection of modified forms detected together by a particular method, the power of the assay can suffer. In this case, it is useful to employ an assay method that distinguishes between forms of a protein and that specifically detects and measures a desired modified form or forms of the protein. Distinguishing different forms of an analyte or specifically detecting a particular form of an analyte is referred to as “resolving” the analyte.

The collection of analytes detected in an assay and the ability to resolve modified forms of a protein of course depends on the methodology used. For example, an immunoassay using a monoclonal antibody will detect all forms of a protein containing the eptiope and will not distinguish between them. However, a sandwich immunoassay that uses two antibodies directed against different epitopes on a protein will detect all forms of the protein that contain both epitope and will not detect those forms that contain only one of the epitopes. Accordingly this method can be useful when the modified forms differ in a terminal amino acid and one of the antibodies is directed to the terminus of one of these forms.

Preferably, the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or a chip. Methods of coupling biomolecules, such as antibodies, to a solid phase are well known in the art. They can employ, for example, bifunctional linking agents, or the solid phase can be derivatized with a reactive group, such as an epoxide or an imidizole, that will bind the molecule on contact. Biospecific capture reagents against different target proteins can be mixed in the same place, or they can be attached to solid phases in different physical or addressable locations. For example, one can load multiple columns with derivatized beads, each column able to capture a single protein cluster. Alternatively, one can pack a single column with different beads derivatized with capture reagents against a variety of protein clusters, thereby capturing all the analytes in a single place. Accordingly, antibody-derivatized bead-based technologies, such as xMAP technology of Luminex (Austin, Tex.) can be used to detect the protein clusters. However, the biospecific capture reagents must be specifically directed toward the members of a cluster in order to differentiate them.

Mass spectrometry is a particularly powerful resolving methodology because different forms of a protein typically have different masses and can be differentiated by mass spectrometry. One useful methodology combines mass spectrometry with immunoassay. First, a biospecific capture reagent (e.g., an antibody, aptamer or Affibody that recognizes the biomarker and modified forms of it) is used to capture the biomarker of interest. Preferably, the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or a chip. After unbound materials are washed away, the captured analytes are detected and/or measured by mass spectrometry. (This method also will also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers.) Then, the captured proteins can be detected by SELDI mass spectrometry or by eluting the proteins from the capture reagent and detecting the eluted proteins by traditional MALDI, SELDI or any other ionization method for mass spectrometry (e.g., electrospray).

Thus, when reference is made herein to detecting a particular protein or to measuring the amount of a particular protein, it means detecting and measuring the protein with or without resolving modified forms of protein. For example, the step of “measuring Apolipoprotein A-IV precursor” includes measuring Apolipoprotein A-IV precursor by means that do not differentiate between various forms of the protein (e.g., certain immunoassays) as well as by means that differentiate some forms from other forms or that measure a specific form of the protein. In contrast, when it is desired to measure a particular form or forms of a protein, the particular form (or forms) is specified. For example, “measuring M7.065159” or a biomarker of 7.065159 kDa means measuring it in a way that distinguishes it from forms of the protein that do not have the characteristic properties identified in Tables 1-5.

Detection of Biomarkers for Dengue

The biomarkers of this invention can be detected by any suitable method. Detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy. Illustrative of optical methods, in addition to microscopy, both confocal and non-confocal, are detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry).

In one aspect, a sample is analyzed by means of a biochip. Biochips generally comprise solid substrates and have a generally planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.

Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems, Inc. (Fremont, Calif.), Zyomyx (Hayward, Calif.), Invitrogen (Carlsbad, Calif.), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK). Examples of such protein biochips are described in the following patents or published patent applications: U.S. Pat. No. 6,225,047 (Hutchens & Yip); U.S. Pat. No. 6,537,749 (Kuimelis and Wagner); U.S. Pat. No. 6,329,209 (Wagner et al.); PCT International Publication No. WO 00/56934 (Englert et al.); PCT International Publication No. WO 03/048768 (Boutell et al.); and U.S. Pat. No. 5,242,828 (Bergstrom et al.).

Detection by Mass Spectrometry

In a preferred aspect, the biomarkers of this invention are detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions. Examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these.

In a further preferred method, the mass spectrometer is a laser desorption/ionization mass spectrometer. In laser desorption/ionization mass spectrometry, the analytes are placed on the surface of a mass spectrometry probe, a device adapted to engage a probe interface of the mass spectrometer and to present an analyte to ionizing energy for ionization and introduction into a mass spectrometer. A laser desorption mass spectrometer employs laser energy, typically from an ultraviolet laser, but also from an infrared laser, to desorb analytes from a surface, to volatilize and ionize them and make them available to the ion optics of the mass spectrometer.

SELDI

A preferred mass spectrometric technique for use in the invention is “Surface Enhanced Laser Desorption and Ionization” or “SELDI,” as described, for example, in U.S. Pat. No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip. This refers to a method of desorption/ionization gas phase ion spectrometry (e.g., mass spectrometry) in which an analyte (here, one or more of the biomarkers) is captured on the surface of a SELDI mass spectrometry probe. There are several versions of SELDI.

One version of SELDI is called “affinity capture mass spectrometry.” It also is called “Surface-Enhanced Affinity Capture” or “SEAC”. This version involves the use of probes that have a material on the probe surface that captures analytes through a non-covalent affinity interaction (adsorption) between the material and the analyte. The material is variously called an “adsorbent,” a “capture reagent,” an “affinity reagent” or a “binding moiety.” Such probes can be referred to as “affinity capture probes” and as having an “adsorbent surface.” The capture reagent can be any material capable of binding an analyte. The capture reagent is attached to the probe surface by physisorption or chemisorption. In certain aspects the probes have the capture reagent already attached to the surface. In other aspects, the probes are pre-activated and include a reactive moiety that is capable of binding the capture reagent, e.g., through a reaction forming a covalent or coordinate covalent bond. Epoxide and acyl-imidizole are useful reactive moieties to covalently bind polypeptide capture reagents such as antibodies or cellular receptors. Nitrilotriacetic acid and iminodiacetic acid are useful reactive moieties that function as chelating agents to bind metal ions that interact non-covalently with histidine containing peptides. Adsorbents are generally classified as chromatographic adsorbents and biospecific adsorbents.

“Chromatographic adsorbent” refers to an adsorbent material typically used in chromatography. Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (e.g., nitrilotriacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents).

“Biospecific adsorbent” refers to an adsorbent comprising a biomolecule, e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, a polysaccharide, a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g., DNA)-protein conjugate). In certain instances, the biospecific adsorbent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific adsorbents are antibodies, receptor proteins and nucleic acids. Biospecific adsorbents typically have higher specificity for a target analyte than chromatographic adsorbents. Further examples of adsorbents for use in SELDI can be found in U.S. Pat. No. 6,225,047. A “bioselective adsorbent” refers to an adsorbent that binds to an analyte with an affinity of at least 10⁻⁸ M.

Protein biochips produced by Ciphergen Biosystems, Inc. comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations. Ciphergen ProteinChip® arrays include NP20 (hydrophilic); H4 and HSO (hydrophobic); SAX-2, Q-10 and LSAX-30 (anion exchange); WCX-2, CM-10 and LWCX-30 (cation exchange); IMAC-3, IMAC-30 and IMAC 40 (metal chelate); and PS-10, PS-20 (reactive surface with acyl-imidizole, epoxide) and PG-20 (protein G coupled through acyl-imidizole). Hydrophobic ProteinChip arrays have isopropyl or nonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anion exchange ProteinChip arrays have quaternary ammonium functionalities. Cation exchange ProteinChip arrays have carboxylate functionalities. Immobilized metal chelate ProteinChip arrays have nitrilotriacetic acid functionalities that adsorb transition metal ions, such as copper, nickel, zinc, and gallium, by chelation. Preactivated ProteinChip arrays have acyl-imidizole or epoxide functional groups that can react with groups on proteins for covalent binding.

Such biochips are further described in: U.S. Pat. No. 6,579,719 (Hutchens and Yip, “Retentate Chromatography,” Jun. 17, 2003); U.S. Pat. No. 6,897,072 (Rich et al., “Probes for a Gas Phase Ion Spectrometer,” Can 24, 2005); U.S. Pat. No. 6,555,813 (Beecher et al., “Sample Holder with Hydrophobic Coating for Gas Phase Mass Spectrometer,” Apr. 29, 2003); U.S. Patent Application No. U.S. 2003 0032043 A1 (Pohl and Papanu, “Latex Based Adsorbent Chip,” Jul. 16, 2002); and PCT International Publication No. WO 03/040700 (Um et al., “Hydrophobic Surface Chip,” Can 15, 2003); U.S. Patent Application No. US 2003/0218130 A1 (Boschetti et al., “Biochips With Surfaces Coated With Polysaccharide-Based Hydrogels,” Apr. 14, 2003) and U.S. Patent Application No. 60/448,467, entitled “Photocrosslinked Hydrogel Surface Coatings” (Huang et al., filed Feb. 21, 2003).

In general, a probe with an adsorbent surface is contacted with the sample for a period of time sufficient to allow the biomarker or biomarkers that can be present in the sample to bind to the adsorbent. After an incubation period, the substrate is washed to remove unbound material. Any suitable washing solutions can be used; preferably, aqueous solutions are employed. The extent to which molecules remain bound can be manipulated by adjusting the stringency of the wash. The elution characteristics of a wash solution can depend, for example, on pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength, and temperature. Unless the probe has both SEAC and SEND properties (as described herein), an energy absorbing molecule then is applied to the substrate with the bound biomarkers.

The biomarkers bound to the substrates are detected in a gas phase ion spectrometer such as a time-of-flight mass spectrometer. The biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions. The detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarker can be determined.

Another version of SELDI is Surface-Enhanced Neat Desorption (SEND), which involves the use of probes comprising energy absorbing molecules that are chemically bound to the probe surface (“SEND probe”). The phrase “energy absorbing molecules” (EAM) denotes molecules that are capable of absorbing energy from a laser desorption/ionization source and, thereafter, contribute to desorption and ionization of analyte molecules in contact therewith. The EAM category includes molecules used in MALDI, frequently referred to as “matrix,” and is exemplified by cinnamic acid derivatives, sinapinic acid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid, and hydroxyaceto-phenone derivatives. In certain aspects, the energy absorbing molecule is incorporated into a linear or cross-linked polymer, e.g., a polymethacrylate. For example, the composition can be a co-polymer of a-cyano-4-methacryloyloxycinnamic acid and acrylate. In another aspect, the composition is a co-polymer of a-cyano-4-methacryloyloxycinnamic acid, acrylate and 3-(tri-ethoxy)silyl propyl methacrylate. In another aspect, the composition is a co-polymer of a-cyano-4-methacryloyloxycinnamic acid and octadecylmethacrylate (“C18 SEND”). SEND is further described in U.S. Pat. No. 6,124,137 and PCT International Publication No. WO 03/64594 (Kitagawa, “Monomers And Polymers Having Energy Absorbing Moieties Of Use In Desorption/Ionization Of Analytes,” Aug. 7, 2003).

SEAC/SEND is a version of SELDI in which both a capture reagent and an energy absorbing molecule are attached to the sample presenting surface. SEAC/SEND probes therefore allow the capture of analytes through affinity capture and ionization/desorption without the need to apply external matrix. The C18 SEND biochip is a version of SEAC/SEND, comprising a C18 moiety which functions as a capture reagent, and a CHCA moiety which functions as an energy absorbing moiety.

Another version of SELDI, called Surface-Enhanced Photolabile Attachment and Release (SEPAR), involves the use of probes having moieties attached to the surface that can covalently bind an analyte, and then release the analyte through breaking a photolabile bond in the moiety after exposure to light, e.g., to laser light (see, U.S. Pat. No. 5,719,060). SEPAR and other forms of SELDI are readily adapted to detecting a biomarker or biomarker profile, pursuant to the present invention.

Other Mass Spectrometry Methods

In another mass spectrometry method, the biomarkers are first captured on a chromatographic resin having chromatographic properties that bind the biomarkers. In the present example, this could include a variety of methods. For example, one could capture the biomarkers on a cation exchange resin, such as CM Ceramic HyperD F resin, wash the resin, elute the biomarkers and detect by MALDI. Alternatively, this method could be preceded by fractionating the sample on an anion exchange resin before application to the cation exchange resin. In another alternative, one could fractionate on an anion exchange resin and detect by MALDI directly. In yet another method, one could capture the biomarkers on an immuno-chromatographic resin that comprises antibodies that bind the biomarkers, wash the resin to remove unbound material, elute the biomarkers from the resin and detect the eluted biomarkers by MALDI or by SELDI. In yet another method, one could isolate the biomarkers using gel elecrophoresis and detect the biomarkers by MALDI OR SELDI.

Data Analysis

Analysis of analytes by time-of-flight mass spectrometry generates a time-of-flight spectrum. The time-of-flight spectrum ultimately analyzed typically does not represent the signal from a single pulse of ionizing energy against a sample, but rather the sum of signals from a number of pulses. This reduces noise and increases dynamic range. This time-of-flight data is then subject to data processing. In Ciphergen's ProteinChip® software, data processing typically includes TOF-to-M/Z transformation to generate a mass spectrum, baseline subtraction to eliminate instrument offsets and high frequency noise filtering to reduce high frequency noise.

Data generated by desorption and detection of biomarkers can be analyzed with the use of a programmable digital computer. The computer program analyzes the data to indicate the number of biomarkers detected, and optionally the strength of the signal and the determined molecular mass for each biomarker detected. Data analysis can include steps of determining signal strength of a biomarker and removing data deviating from a predetermined statistical distribution. For example, the observed peaks can be normalized, by calculating the height of each peak relative to some reference.

The computer can transform the resulting data into various formats for display. The standard spectrum can be displayed, but in one useful format only the peak height and mass information are retained from the spectrum view, yielding a cleaner image and enabling biomarkers with nearly identical molecular weights to be more easily seen. In another useful format, two or more spectra are compared, conveniently highlighting unique biomarkers and biomarkers that are up- or down-regulated between samples. Using any of these formats, one can readily determine whether a particular biomarker is present in a sample.

Analysis generally involves the identification of peaks in the spectrum that represent signal from an analyte. Peak selection can be done visually, but software is available, as part of Ciphergen's ProteinChip® software package, that can automate the detection of peaks. In general, this software functions by identifying signals having a signal-to-noise ratio above a selected threshold and labeling the mass of the peak at the centroid of the peak signal. In one useful application, many spectra are compared to identify identical peaks present in some selected percentage of the mass spectra. One version of this software clusters all peaks appearing in the various spectra within a defined mass range, and assigns a mass (M/Z) to all the peaks that are near the mid-point of the mass (M/Z) cluster.

Software used to analyze the data can include code that applies an algorithm to the analysis of the signal to determine whether the signal represents a peak in a signal that corresponds to a biomarker according to the present invention. The software also can subject the data regarding observed biomarker peaks to classification tree or ANN analysis, to determine whether a biomarker peak or combination of biomarker peaks is present that indicates the status of the particular clinical parameter under examination. Analysis of the data can be “keyed” to a variety of parameters that are obtained, either directly or indirectly, from the mass spectrometric analysis of the sample. These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks, and other arithmetic manipulations of peak height data.

General Protocol for SELDI Detection of Biomarkers for Dengue

A preferred protocol for the detection of the biomarkers of this invention is as follows. The biological sample to be tested, e.g., serum, preferably is subject to pre-fractionation before SELDI analysis. This simplifies the sample and improves sensitivity. A preferred method of pre-fractionation involves contacting the sample with an anion exchange chromatographic material, such as Q HyperD (BioSepra, SA). The bound materials are then subject to stepwise pH elution using buffers at pH 9, pH 7, pH 5 and pH 4. (The fractions in which the biomarkers are eluted also are indicated in Tables 1-2, and 4) Various fractions containing the biomarker are collected.

The sample to be tested (preferably pre-fractionated) is then contacted with an affinity capture probe comprising an cation exchange adsorbent (preferably a WCX ProteinChip array (Ciphergen Biosystems, Inc.)) or an IMAC adsorbent (preferably an IMAC3 ProteinChip array (Ciphergen Biosystems, Inc.)). The probe is washed with a buffer that will retain the biomarker while washing away unbound molecules. The biomarkers are detected by laser desorption/ionization mass spectrometry.

Alternatively, if antibodies that recognize the biomarker are available, these can be attached to the surface of a probe, such as a pre-activated PS10 or PS20 ProteinChip array (Ciphergen Biosystems, Inc.). These antibodies can capture the biomarkers from a sample onto the probe surface. Then the biomarkers can be detected by, e.g., laser desorption/ionization mass spectrometry.

Detection by Immunoassay

In another aspect of the invention, the biomarkers of the invention are measured by a method other than mass spectrometry or other than methods that rely on a measurement of the mass of the biomarker. In one such aspect that does not rely on mass, the biomarkers of this invention are measured by immunoassay. Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers. Antibodies can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art.

This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays. Nephelometry is an assay done in liquid phase, in which antibodies are in solution. Binding of the antigen to the antibody results in changes in absorbance, which is measured. In the SELDI-based immunoassay, a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated ProteinChip array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.

Determination of Subject Dengue Status

Single Markers

The biomarkers of the invention can be used in diagnostic tests to assess dengue status in a subject, e.g., to diagnose Dengue. The phrase “Dengue status” includes any distinguishable manifestation of the disease, including non-disease. For example, disease status includes, without limitation, the presence or absence of disease (e.g., dengue v. non dengue or Dengue v. other disease (e.g., OFIs), the risk of developing disease, the stage of the disease, the progress of disease (e.g., progress of disease or remission of disease over time) and the effectiveness or response to treatment of disease. The status of the subject can inform the practitioner about what status set is being distinguished. For example, a subject that presents with signs of a disease could be classed into Dengue v. non-Dengue disease, while a person exposed to a situation in which Dengue infection is possible and who is presenting with signs of Dengue infection could be classified into Dengue v. non-Dengue. Based on this status, further procedures can be indicated, including additional diagnostic tests or therapeutic procedures or regimens.

The power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic (“ROC”) curve. Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. An ROC curve provides the sensitivity of a test as a function of 1-specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test. Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.

The biomarkers of this invention show a statistical difference in different dengue statuses of at least p≦0.05, p≦10⁻², p≦10⁻³, p≦10⁻⁴ or p≦10⁻⁵. Diagnostic tests that use these biomarkers alone or in combination show a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.

Each biomarker listed in Tables 1-5 and 17-24 is differentially present in dengue, and, therefore, each is individually useful in aiding in the determination of dengue status. The method involves, first, measuring the selected biomarker in a subject sample using the methods described herein, e.g., capture on a SELDI biochip followed by detection by mass spectrometry and, second, comparing the measurement with a diagnostic amount or cut-off that distinguishes a positive dengue status from a negative dengue status. The diagnostic amount represents a measured amount of a biomarker above which or below which a subject is classified as having a particular dengue status, e.g. DF, DHF, DSS. For example, if the biomarker is up-regulated compared to normal during dengue, then a measured amount above the diagnostic cutoff provides a diagnosis of dengue status. Alternatively, if the biomarker is down-regulated during dengue, then a measured amount below the diagnostic cutoff provides a diagnosis of dengue status. As is well understood in the art, by adjusting the particular diagnostic cut-off used in an assay, one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician. The particular diagnostic cut-off can be determined, for example, by measuring the amount of the biomarker in a statistically significant number of samples from subjects with the different dengue statuses, as was done here, and drawing the cut-off to suit the diagnostician's desired levels of specificity and sensitivity.

Combinations of Markers

While individual biomarkers are useful diagnostic biomarkers, it has been found that a combination of biomarkers can provide greater predictive value of a particular status than single biomarkers alone. Specifically, the detection of a plurality of biomarkers in a sample can increase the sensitivity and/or specificity of the test. A combination of at least two biomarkers is sometimes referred to as a “biomarker profile” or “biomarker fingerprint.”

Presence of Dengue

In one aspect, this invention provides methods for determining the presence or absence of dengue in a subject (status: dengue v. non-dengue). The presence or absence of dengue is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.

Determining Risk of Developing Disease

In one aspect, this invention provides methods for determining the risk of developing disease in a subject. Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium or low. The risk of developing a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.

Determining Stage of Disease

In one aspect, this invention provides methods for determining the stage of disease in a subject. Each stage of the disease has a characteristic amount of a biomarker or relative amounts of a set of biomarkers (a pattern). The stage of a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular stage.

Determining Course (Progression/Remission) of Disease

In one aspect, this invention provides methods for determining the course of disease in a subject. Disease course refers to changes in disease status over time, including disease progression (worsening) and disease regression (improvement). Over time, the amounts or relative amounts (e.g., the pattern) of the biomarkers changes. Therefore, the trend of these markers, either increased or decreased over time toward diseased or non-diseased indicates the course of the disease. Accordingly, this method involves measuring one or more biomarkers in a subject at least two different time points, e.g., a first time and a second time, and comparing the change in amounts, if any. The course of disease is determined based on these comparisons.

Subject Management

In certain aspects of the methods of qualifying dengue status, the methods further comprise managing subject treatment based on the status. Such management includes the actions of the physician or clinician subsequent to determining dengue status. For example, if a physician makes a diagnosis of dengue, then a certain regime of treatment, such as prescription or administration of paracetamol, antipyretics or a combination thereof, might follow. Alternatively, a diagnosis of non-dengue might be followed with further testing to determine a specific disease that might the patient might be suffering from. Also, if the diagnostic test gives an inconclusive result on dengue status, further tests can be called for.

The methods described herein can be used in combination with and other tests and/or methods that are used to qualify dengue status in a subject. For example, in certain aspects, the methods described herein are used to determine whether or not a subject has an increased likelihood of having dengue. These methods can be used in combination with other tests that are useful for either diagnosing dengue in a subject or ruling out other diagnoses.

Additional aspects of the invention relate to the communication of assay results or diagnoses or both to technicians, physicians or patients, for example. In certain aspects, computers will be used to communicate assay results or diagnoses or both to interested parties, e.g., physicians and their patients. In some aspects, the assays will be performed or the assay results analyzed in a country or jurisdiction which differs from the country or jurisdiction to which the results or diagnoses are communicated.

In a preferred aspect of the invention, a diagnosis based on the presence or absence in a test subject of any the biomarkers of Table 1-5, and 17-24 is communicated to the subject as soon as possible after the diagnosis is obtained. The diagnosis can be communicated to the subject by the subject's treating physician. Alternatively, the diagnosis can be sent to a test subject by email or communicated to the subject by phone. A computer can be used to communicate the diagnosis by email or phone. In certain aspects, the message containing results of a diagnostic test can be generated and delivered automatically to the subject using a combination of computer hardware and software which will be familiar to artisans skilled in telecommunications. One example of a healthcare-oriented communications system is described in U.S. Pat. No. 6,283,761; however, the present invention is not limited to methods which utilize this particular communications system. In certain aspects of the methods of the invention, all or some of the method steps, including the assaying of samples, diagnosing of diseases, and communicating of assay results or diagnoses, can be carried out in diverse (e.g., foreign) jurisdictions.

Determining Therapeutic Efficacy of Pharmaceutical Drug

In another aspect, this invention provides methods for determining the therapeutic efficacy of a pharmaceutical drug. These methods are useful in performing clinical trials of the drug, as well as monitoring the progress of a patient on the drug. Therapy or clinical trials involve administering the drug in a particular regimen. The regimen can involve a single dose of the drug or multiple doses of the drug over time. The doctor or clinical researcher monitors the effect of the drug on the patient or subject over the course of administration. If the drug has a pharmacological impact on the condition, the amounts or relative amounts (e.g., the pattern or profile) of the biomarkers of this invention changes toward a non-disease profile. One can follow the course of the amounts of these biomarkers in the subject during the course of treatment. Accordingly, this method involves measuring one or more biomarkers in a subject receiving drug therapy, and correlating the amounts of the biomarkers with the disease status of the subject. One aspect of this method involves determining the levels of the biomarkers at least two different time points during a course of drug therapy, e.g., a first time and a second time, and comparing the change in amounts of the biomarkers, if any. For example, the biomarkers can be measured before and after drug administration or at two different time points during drug administration. The effect of therapy is determined based on these comparisons. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications.

Generation of Classification Algorithms for Qualifying Dengue Status

In some aspects, data derived from the spectra (e.g., mass spectra or time-of-flight spectra) that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre-classified. The data that are derived from the spectra and are used to form the classification model can be referred to as a “training data set.” Once trained, the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).

The training data set that is used to form the classification model can comprise raw data or pre-processed data. In some aspects, raw data can be obtained directly from time-of-flight spectra or mass spectra, and then can be optionally “pre-processed” as described above.

Classification models can be formed using any suitable statistical classification (or “learning”) method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods can be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000, the teachings of which are incorporated by reference.

In supervised classification, training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data can then be applied to the learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART—classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).

A preferred supervised classification method is a recursive partitioning process. Recursive partitioning processes use recursive partitioning trees to classify spectra derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent Application No. 2002 0138208 A1 to Paulse et al., “Method for analyzing mass spectra.”

In other aspects, the classification models that are created can be formed using unsupervised learning methods. Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived. Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into “clusters” or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other. Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm.

Learning algorithms asserted for use in classifying biological information are described, for example, in PCT International Publication No. WO 01/31580 (Barnhill et al., “Methods and devices for identifying patterns in biological systems and methods of use thereof”), U.S. Patent Application No. 2002 0193950 A1 (Gavin et al., “Method or analyzing mass spectra”), U.S. Patent Application No. 2003 0004402 A1 (Hitt et al., “Process for discriminating between biological states based on hidden patterns from biological data”), and U.S. Patent Application No. 2003 0055615 A1 (Zhang and Zhang, “Systems and methods for processing biological expression data”).

The classification models can be formed on and used on any suitable digital computer. Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, Windows™ or Linux™ based operating system. The digital computer that is used can be physically separate from the mass spectrometer that is used to create the spectra of interest, or it can be coupled to the mass spectrometer.

The training data set and the classification models according to aspects of the invention can be embodied by computer code that is executed or used by a digital computer. The computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, and the like, and can be written in any suitable computer programming language including C, C++, visual basic, and the like

The learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, or for finding new biomarkers for dengue. The classification algorithms, in turn, form the base for diagnostic tests by providing diagnostic values (e.g., cut-off points) for biomarkers used singly or in combination.

Compositions of Matter

In another aspect, this invention provides compositions of matter based on the biomarkers of this invention.

In one aspect, this invention provides biomarkers of this invention in purified form. Purified biomarkers have utility as antigens to raise antibodies. Purified biomarkers also have utility as standards in assay procedures. As used herein, a “purified biomarker” is a biomarker that has been isolated from other proteins and peptdies, and/or other material from the biological sample in which the biomarker is found. Biomarkers can be purified using any method known in the art, including, but not limited to, mechanical separation (e.g., centrifugation), ammonium sulphate precipitation, dialysis (including size-exclusion dialysis), size-exclusion chromatography, affinity chromatography, anion-exchange chromatography, cation-exchange chromatography, and methal-chelate chromatography. Such methods can be performed at any appropriate scale, for example, in a chromatography column, or on a biochip.

In another aspect, this invention provides a biospecific capture reagent, optionally in purified form, that specifically binds a biomarker of this invention. In one aspect, the biospecific capture reagent is an antibody. Such compositions are useful for detecting the biomarker in a detection assay, e.g., for diagnostics.

In another aspect, this invention provides an article comprising a biospecific capture reagent that binds a biomarker of this invention, wherein the reagent is bound to a solid phase. For example, this invention contemplates a device comprising bead, chip, membrane, monolith or microtiter plate derivatized with the biospecific capture reagent. Such articles are useful in biomarker detection assays.

In another aspect this invention provides a composition comprising a biospecific capture reagent, such as an antibody, bound to a biomarker of this invention, the composition optionally being in purified form. Such compositions are useful for purifying the biomarker or in assays for detecting the biomarker.

In another aspect, this invention provides an article comprising a solid substrate to which is attached an adsorbent, e.g., a chromatographic adsorbent or a biospecific capture reagent, to which is further bound a biomarker of this invention. In one aspect, the article is a biochip or a probe for mass spectrometry, e.g., a SELDI probe. Such articles are useful for purifying the biomarker or detecting the biomarker.

Kits for Detection of Biomarkers for Dengue

In another aspect, the present invention provides kits for qualifying dengue status, which kits are used to detect biomarkers according to the invention. In one aspect, the kit comprises a solid support, such as a chip, a microtiter plate or a bead or resin having a capture reagent attached thereon, wherein the capture reagent binds a biomarker of the invention. Thus, for example, the kits of the present invention can comprise mass spectrometry probes for SELDI, such as ProteinChip® arrays. In the case of biospecfic capture reagents, the kit can comprise a solid support with a reactive surface, and a container comprising the biospecific capture reagent.

The kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagent and the washing solution allows capture of the biomarker or biomarkers on the solid support for subsequent detection by, e.g., mass spectrometry. The kit can include more than type of adsorbent, each present on a different solid support.

In a further aspect, such a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions can inform a consumer about how to collect the sample, how to wash the probe or the particular biomarkers to be detected.

In yet another aspect, the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.

Use of Biomarkers for Dengue in Screening Assays and Methods of Treating Dengue

The methods of the present invention have other applications as well. For example, the biomarkers can be used to screen for compounds that modulate the expression of the biomarkers in vitro or in vivo, which compounds in turn can be useful in treating or preventing dengue in patients. In another example, the biomarkers can be used to monitor the response to treatments for dengue. In yet another example, the biomarkers can be used in heredity studies to determine if the subject is at risk for developing dengue.

Thus, for example, the kits of this invention could include a solid substrate having a hydrophobic function, such as a protein biochip (e.g., a Ciphergen H50 ProteinChip array, e.g., ProteinChip array) and a sodium acetate buffer for washing the substrate, as well as instructions providing a protocol to measure the biomarkers of this invention on the chip and to use these measurements to diagnose dengue.

Compounds suitable for therapeutic testing can be screened initially by identifying compounds which interact with one or more biomarkers listed in Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24. By way of example, screening might include recombinantly expressing a biomarker listed in Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, purifying the biomarker, and affixing the biomarker to a substrate. Test compounds would then be contacted with the substrate, typically in aqueous conditions, and interactions between the test compound and the biomarker are measured, for example, by measuring elution rates as a function of salt concentration. Certain proteins can recognize and cleave one or more biomarkers of Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, in which case the proteins can be detected by monitoring the digestion of one or more biomarkers in a standard assay, e.g., by gel electrophoresis of the proteins.

In a related aspect, the ability of a test compound to inhibit the activity of one or more of the biomarkers of Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can be measured. One of skill in the art will recognize that the techniques used to measure the activity of a particular biomarker will vary depending on the function and properties of the biomarker. For example, an enzymatic activity of a biomarker can be assayed provided that an appropriate substrate is available and provided that the concentration of the substrate or the appearance of the reaction product is readily measurable. The ability of potentially therapeutic test compounds to inhibit or enhance the activity of a given biomarker can be determined by measuring the rates of catalysis in the presence or absence of the test compounds. The ability of a test compound to interfere with a non-enzymatic (e.g., structural) function or activity of one of the biomarkers of Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can also be measured. For example, the self-assembly of a multi-protein complex which includes one of the biomarkers of Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can be monitored by spectroscopy in the presence or absence of a test compound. Alternatively, if the biomarker is a non-enzymatic enhancer of transcription, test compounds which interfere with the ability of the biomarker to enhance transcription can be identified by measuring the levels of biomarker-dependent transcription in vivo or in vitro in the presence and absence of the test compound.

Test compounds capable of modulating the activity of any of the biomarkers of Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can be administered to patients who are suffering from or are at risk of developing dengue. For example, the administration of a test compound which increases the activity of a particular biomarker can decrease the risk of dengue in a patient if the activity of the particular biomarker in vivo prevents the accumulation of proteins for dengue. Conversely, the administration of a test compound which decreases the activity of a particular biomarker can decrease the risk of dengue in a patient if the increased activity of the biomarker is responsible, at least in part, for the onset of dengue.

In an additional aspect, the invention provides a method for identifying compounds useful for the treatment of disorders such as dengue which are associated with increased levels of modified forms of the biomarkers in Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24. For example, in one aspect, cell extracts or expression libraries can be screened for compounds which catalyze the cleavage of a full-length biomarker to form truncated forms of the biomarker. In one aspect of such a screening assay, cleavage of the biomarker can be detected by attaching a fluorophore to the biomarker which remains quenched when the biomarker is uncleaved but which fluoresces when the protein is cleaved. Alternatively, a version of full-length biomarker modified so as to render the amide bond between amino acids x and y uncleavable can be used to selectively bind or “trap” the cellular protesase which cleaves full-length biomarker at that site in vivo. Methods for screening and identifying proteases and their targets are well-documented in the scientific literature, e.g., in Lopez-Ottin et al. (Nature Reviews, 2002, 3:509-519).

In yet another aspect, the invention provides a method for treating or reducing the progression or likelihood of a disease, e.g., dengue, which is associated with the increased levels of a truncated biomarker. For example, after one or more proteins have been identified which cleave the full-length biomarker, combinatorial libraries can be screened for compounds which inhibit the cleavage activity of the identified proteins. Methods of screening chemical libraries for such compounds are well-known in art. See, e.g., Lopez-Otin et al. (2002). Alternatively, inhibitory compounds can be intelligently designed based on the structure of the biomarker.

At the clinical level, screening a test compound includes obtaining samples from test subjects before and after the subjects have been exposed to a test compound. The levels in the samples of one or more of the biomarkers listed in Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can be measured and analyzed to determine whether the levels of the biomarkers change after exposure to a test compound. The samples can be analyzed by mass spectrometry, as described herein, or the samples can be analyzed by any appropriate means known to one of skill in the art. For example, the levels of one or more of the biomarkers listed in Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can be measured directly by Western blot using radio- or fluorescently-labeled antibodies which specifically bind to the biomarkers. Alternatively, changes in the levels of mRNA encoding the one or more biomarkers can be measured and correlated with the administration of a given test compound to a subject. In a further aspect, the changes in the level of expression of one or more of the biomarkers can be measured using in vitro methods and materials. For example, human tissue cultured cells which express, or are capable of expressing; one or more of the biomarkers of Table 1, 2, 3, 4, 5, 17, 18, 19, 20, 21, 22, 23, or 24, can be contacted with test compounds. Subjects who have been treated with test compounds will be routinely examined for any physiological effects which can result from the treatment. In particular, the test compounds will be evaluated for their ability to decrease disease likelihood in a subject. Alternatively, if the test compounds are administered to subjects who have previously been diagnosed with dengue, test compounds will be screened for their ability to slow or stop the progression of the disease.

The invention will be further described with reference to the following exemplary aspects; however, it is to be understood that the invention is not limited to such exemplary aspects.

Exemplary Aspects

Below are examples of specific aspects for carrying out the present invention. The examples are offered for illustrative purposes only, and are not intended to limit the scope of the present invention in any way. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperatures, and the like), but some experimental error and deviation should, of course, be allowed for.

The practice of the present invention will employ, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA techniques and pharmacology, within the skill of the art. Such techniques are explained fully in the literature. See, e.g., T. E. Creighton, Proteins: Structures and Molecular Properties (W.H. Freeman and Company, 1993); A. L. Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al., Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.); Remington's Pharmaceutical Sciences, 18th Edition (Easton, Pa.: Mack Publishing Company, 1990); Carey and Sundberg Advanced Organic Chemistry 3^(rd) Ed. (Plenum Press) Vols A and B(1992).

Example 1 Discovery of Dengue Biomarkers

Two complimentary approaches to identifying potential biomarkers for the diagnosis or prognosis of dengue have been taken: 1) SELDI-based and 2) gel-based. Based on estimated molecular weight, there is an overlap of biomarkers identified by both approaches (Tables 1-5). Similar methods for the discovery of biomarkers for babesia were used in U.S. provisional application Ser. No. 60/749,449 filed on Dec. 12, 2005 and U.S. provisional application Ser. No. 60/752,285 filed on Dec. 20, 2005, both of which are herein incorporated by reference for all purposes. The discovered biomarkers are shown in Tables 1-5, and Tables 17-24.

Sample Collection

Sample collection was previously peformed by Takol.

Plasma samples from pediatric That patients were obtained. For each dengue infected patient, 3 blood samples were taken at 3 different time points: t1 (1^(st) day of admission), t2 (fever decreased to normal), t3 (convalescence stage 30 days after admission). Each probable dengue diagnosis was confirmed and the serotype as well as the type (primary or secondary) of the infection recorded. Samples of patients with other febrile illnesses (OFIs) were also collected to be used as controls. The samples were stored at −80° C. (Table 6). Table 6 shows a list of specimens collected in Thailand from pediatric patients. The list of 15 controls is not included.

TABLE 6 Classification of DENV infected pediatric patients. TABLE 6 DENV Primary Infection Secondary Infection Serotype DF DHF DF DHF 1 3 1 7 6 2 3 6 7 10 3 5 0 2 4 4 1 2 8 7 Total 12 9 24 27

Preparation and Fractionation of Serum Samples

Preparation and fractionation of serum samples was previously performed by Takol.

Fractionation of serum samples was performed with the use of the Biomek 2000 Laboratory Automation Workstation (Beckman Coulter, USA) using software protocols provided by Ciphergen (Ciphergen Biosystems, Fremont, Calif., USA). An Expression Difference Mapping Kit (Ciphergen Biosystems, Fremont, Calif., USA) was also used according to the manufacturer's instructions. Six fractions obtained through Fractionation of serum samples was performed with the use of the Biomek 2000 Laboratory Automation Workstation (Beckman Coulter, USA) using software protocols provided by Ciphergen (Ciphergen Biosystems, Fremont, Calif., USA). An Expression Difference Mapping Kit (Ciphergen Biosystems, Fremont, Calif., USA) was also used according to the manufacturer's instructions. Six fractions obtained through isoelectric point separation were obtained and collected using different buffers: F1 (pH 9), F2 (pH 7), F3 (pH5), F4 (pH 4), F5 (pH 3), F6 (organic solvent). The fractions were stored at −80° C.

SELDI Analysis

Protein Binding Using ProteinChip Arrays

Protein binding using ProteinChip Arrays was previously performed by Takol.

The following chip binding protocol was followed and the samples were processed using an IMAC-3 ProteinChip Array according to the protocol below:

Chip Binding Protocol

Weak Cation Exchange (WCX2) ProteinChip Array

Materials:

Bioprocessor

WCX-2 chip

Vortex

CM low stringency buffer

Deionized water

EAM solution

-   -   1. Assemble the WCX-2 protein chip in the bioprocessor.     -   2. Add 150 ul of CM low stringency buffer to each well.     -   3. Vortex for 5 minutes (speed 100 rpm) at room temperature.     -   4. Remove the buffer from the wells.     -   5. Repeat steps 2 to 3 for a total of 2 washes.     -   6. Add 90 ul of CM low stringency buffer to each well.     -   7. Add 10 ul of sample (fractions) to each well.     -   8. Vortex for 30 minutes (speed 100 rpm) at room temperature.     -   9. Remove the samples from the wells.     -   10. Wash each well with 150 ul CM low stringency buffer.     -   11. Vortex for 5 minutes (100 rpm).     -   12. Repeat twice for a total of three buffer washes.     -   13. Remove the washing buffer from the wells and rinse each well         with deionized water.     -   14. Drain the wells and remove the chip from the bioprocessor.     -   15. Allow the chip to air dry.     -   16. Apply 0.5-1 ul of EAM solution per spot twice.     -   17. Allow to air dry after each application.     -   18. Analyze the chip.

Processing Samples Using an IMAC-3 ProteinChip Array

Material:

Bioprocessors

IMAC Chips

Pap Pen

Votex (VWR VX-2500 Multitube Vortexer)

IMAC3 Chip Buffer:

A) Binding Buffer: 100 mM Sodium Phosphate+0.5M NaCl pH 7.0+0.1% Triton X

B) Charging Buffer (Copper): 100 mM CuSO₄+0.1% Triton X 20

C) Neutralizing Buffer:100 mM NaAcetate pH 4.0+0.1% Triton X 20

-   -   1. Place Chip in bioprocessor     -   2. Load IMAC chips with copper: Apply 50 μl/well of 100 mM CuSO₄     -   3. Vortex 5 min (speed 100 rpm) at room temperature     -   4. Remove CuSO₄     -   5. Wash with water 120 μl/well     -   6. Vortex 5 min (speed 100 rpm)     -   7. Neutralize chips: Add 50 μl/well of 100 mM NaAcetate pH 4.0     -   8. Remove solution     -   9. Wash with water 120 μl/well     -   10. Vortex 5 min (speed 100 rpm)     -   11. Repeat steps 9 & 10 a further two times     -   12. Equilibrate Chips: Add 120 μl Binding Buffer (PBS/0.5 M         NaCl, pH 7.5)     -   13. Vortex 5 min (100 rpm)     -   14. Bind fractions to chips: Discard waste and add 80 μl Binding         Buffer and 20 μl of fractions (containing samples)     -   15. Vortex 45-60 min (100 rpm)     -   16. Discard and wash (PBS/0.5M NaCl, 150 μl/well)     -   17. Vortex 5 min (100 rpm)     -   18. Repeat steps 16 & 17 a further two times     -   19. Rinse chip with dH₂O (150 μl/well)     -   20. Add Matrix: Remove bioproceesor top and gasket     -   21. Rinse the Chips quickly with dH₂O     -   22. Dry chips     -   23. Circle spots with PAP pen     -   24. Add 0.5 μl SPA to Chips two times (air dry the spots between         addition) Ciphergen normally supplies EAM as 5 mg of dried         powder in a tube. Add 100 μl of 100% Acetonitrile (final         concentration 50% ACN)+50 μl 2% Trifluoroacetic acid (final         conc. 0.5% TFA)+50 μl dH₂O.         -   Vortex 1 min (high speed) and leave it in the bunch for 5             min         -   Spin 2 min at high speed to pellet any particulates     -   25. Dry     -   26. Read within 1 hour

Protein binding to ProteinChip Arrays was performed using the Biomek 2000 Laboratory Automation Workstation (Beckman Coulter) and protein binding software protocols provided by Ciphergen Biosystems. Immobilized affinity capture (IMAC3), weak cation-exchange (CM10) and hydrophobic (H50) ProteinChip Array types (eight spot format) were used (Ciphergen Biosystems). ProteinChip arrays were analyzed in the ProteinChip Biology System reader (model PBS IIc, Ciphergen Biosystems).

Reading and Analysis of ProteinChip Arrays

To initially compare data between different diseases tested, arrays were read at low (intensity=175, sensitivity=7, optimization range=2000-20,000 Da, high range=50,000 Da) and high (intensity=175, sensitivity=8, optimization range=20,000-50,000 Da, high range=150,000 Da) laser settings. The data was analyzed using ProteinChip software (version 3.2.1) and Ciphergen Express Data Manager (version 2.1) (Ciphergen Biosystems).

All data were imported into Ciphergen Express (CE) and grouped according to each condition (e.g., DHF fraction 1 bound to a WCX2 array, read at low laser intensity). Each data set was calibrated using an equation generated from a spectrum of protein standards, which were collected at the same laser intensity as the collected sample data.

The Baseline for all data was set at 15, and Noise set at 2000 Da (for arrays read at low laser energy) or 10,000 Da (high laser energy). Sample spectra for each group were normalized using a specific set of conditions. Arrays read at low laser intensity were normalized between 2000-100,000 Da, and 10,000-200,000 Da for high laser intensity. An external normalization coefficient of 0.2 was applied for both conditions. As a quality control measure for the comparison of spectra processed on different days, the average normalization factor was first calculated for all spectra within the condition. Any spectra that did not fall within twice the overall average normalization factor were discarded from the analysis.

Peak and Cluster detection (EDM) was then performed for both low and high laser intensities for each sample condition. A distinct set of variables were set for each of the samples collected depending on if they were obtained using low or high laser intensity.

The first set of comparisons was carried between control1 and 1DF1 and 1DHF1, control2 and 1DF2 and 1DHF2, 1DF1 and 1DHF1, 1DF2 and 1DHF2, 1DF3 and 1DHF3 plasma samples. After the first-pass analysis, all clusters found to have a p-value ≦0.05 were visually inspected for peak quality. High quality protein peaks were manually relabelled. A second-pass analysis was carried out; the EDM was run again using only user-detected peaks. Using Biomarker Pattern Software (BPS), a decision analysis software, combination of these candidate biomarkers was determined as well as their specificity and sensitivity using pooled data from 1DF1, 1DF2, 1DHF1 and 1DHF2 versus pooled data from control 1 and 2. These candidate biomarkers represent potential diagnostic biomarkers.

A second set of comparisons was carried out between secondary 2DF1 and 2DF1, 2DF2 and 2DHF2, 2DF3 and 2DHF3. The same first- and second-pass analysis protocol was followed with the same p-value limit.

Since the samples from primary and secondary infections were carried on 2 separate bioprocessors on 2 different days, the quality method described above was applied before the following analyses were carried out. A third set of comparisons was carried out between primary and secondary DF at each 3 time point as well as between primary and secondary DHF at each 3 time points. A comparison between control1 and 2DF1 and 2DHF1 as well as between control2 and 2DF2 and 2DHF2 was also carried. The same first- and second-pass analysis protocol was followed but only clusters found to have a p-value ≦0.005 were kept. BPS analysis was also carried using the same comparisons above. FIGS. 1-11, 13-34, and Tables 3-4, 7-16, 17-24 show the results of a SELDI-based biomarker discovery study. The biomarkers presented in these tables and figures can be used in all aspects of the present invention. F1CSL and F1CSH refers to Fraction 1, WCX2, SPA, Low or High intensity; F1ISL and F1ISH refer to Fraction 1, IMAC, SPA, Low or High intensity; F3CSL and F3CSH refer to Fraction 3, WCX2, SPA, Low or High intensity; F5CSL or F5CSH refer to Fraction 5, WCX2, SPA, Low or High intensity; F51SL and F51SH refer to Fraction 5, IMAC, SPA, Low or High intensity; F6CSL and F6CSH refer to Fraction 6, WCX2, SPA, Low or High intensity; and F61SL and F6ISH refer to Fraction 6, IMAC, SPA, Low or High intensity.

ZOOM Fractionation and SDS PAGE

Control 1 and 2 samples were pooled together and 1DF1,2 samples were pooled with 1DHF1,2. The plasma samples were prepared following Invitrogen's recommendations. 650 μl of the prepared samples were dispensed in 5 of the ZOOM® IEF Fractionator chambers. The ZOOM was run under standard conditions (100V for 20 min, 200V for 80 min, and 600V for 80 min). Once completed, the fractions from each chamber were kept at −20° C.

40 μl of for each fraction was desalted. Each aliquot was run on a Denaturing 4-12% Bis-Tris NuPAGE Gel Electrophoresis using Mark12 MW Marker 1× (Invitrogen) as the molecular weight ladder. The gel was run at 200V for 45 min with an expected current of 100-125 mA at the beginning and 60-80 mA towards the end. The gel was stained using a Coomassie stain for 2 days. It was destained with MiliQ water until band visualization was satisfying. The gel was kept in acetic acid. The candidate biomarkers were cut and kept in 2% acetic acid tubes and were sent for sequencing using mass spectrometry. Tables 1-2 and FIG. 12 show the results of a biomarker gel-based discovery study. The biomarkers presented in these tables and figures can be used in all aspects of the present invention.

While the invention has been particularly shown and described with reference to a preferred aspect and various alternate aspects, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.

All references, issued patents and patent applications cited within the body of the instant specification are hereby incorporated by reference in their entirety, for all purposes.

TABLE 7

  (A) Variable importance of other potential splitter as predicted by BPS in the F1CSL fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F1CSL either using BPS or CE. The values highlighted in gray indicate their irrelevance as a potential biomarker at t1 of DENV infection.

TABLE 8 Table 8. (A) Variable importance of other potential splitter as predicted by BPS in the F1CSH (fraction 1 using CM10 at high laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F1CSH either using BPS or CE. A Variable Predicted Importance MW (Da) (%) 238240 100.00%  23260  78.50% B Predicted Control 1 vs DF1_DHF1 Control 2 vs DF2_DHF2 MW (Da) p-value ROC value p-value ROC value 11203 0.00005 0.12667 0.00695 0.22851 11605 0.00010 0.12667 0.00473 0.22851 23260 0.00032 0.87333 0.00006 0.90724 23824 0.00022 0.85000 0.00015 0.90724

TABLE 9

  (A) Variable importance of other potential splitter as predicted by BPS in the F1ISL (fraction 1 using IMAC at low laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decresases to normal). (B) p-value and ROC value for all candidate biomarkers found in F1ISL either using BPS or CE. The values highlighted in gray indicate their irrelevance as a potential biomarker at t1 of DENV infection.

TABLE 10 Table 10. (A) Variable importance of other potential splitter as predicted by BPS in the F1ISH (fraction 1 using IMAC at high laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F1ISH either using BPS or CE. A Variable Predicted Importance MW (Da) (%) 23105 100.00 23638 76.33 56622 72.35 B Predicted Control 1 vs DF1_DHF1 Control 2 vs DF2_DHF2 MW (Da) p-value ROC value p-value ROC value 10614 0.02586 0.23958 0.00007 0.09167 10634 0.00255 0.16667 0.00090 0.16667 10649 0.04109 0.27083 0.00157 0.19167 23105 0.00059 0.90625 0.00501 0.78125 23638 0.00098 0.85417 0.01019 0.75625 56622 0.00159 0.14063 0.00719 0.19167

TABLE 11

  (A) Variable importance of other potential splitter as predicted by BPS in the F5CSL (fraction 5 using CM10 at low laser intensity) fraction to discrimiated between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F5CSL either using BPS or CE. The values highlighted in gray indicate their irrelevance as a potential biomarker at t1 of DENV infection. *Splitter used in BPS analysis.

TABLE 12 Table 12. (A) Variable importance of other potential splitter as predicted by BPS in the F5CSH (fraction 5 using CM10 at high laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F5CSH either using BPS or CE. A Variable Predicted Importance MW (Da) (%) 13294 100.00 13092 78.78 13325 68.42 B Predicted Control 1 vs DF1_DHF1 Control 2 vs DF2_DHF2 MW (Da) p-value ROC value p-value ROC value 12919 0.00031 0.12821 0.00042 0.14254 13092 0.00006 0.07692 0.00001 0.03728 13294 0.00001 0.02564 0.00001 0.01096 13325 0.00003 0.05128 0.00002 0.03728

TABLE 13

  (A) Variable importance of other potential splitter as predicted by BPS in the F6CSL (fraction 6 using CM10 at low laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F6CSL either using BPS or CE. The values highlighted in gray indicate their irrelevance as a potential biomarker at t1 of DENV infection.

TABLE 14 Table 14. (A) Variable importance of other potential splitter as predicted by BPS in the F6CSH (fraction 6 using CM10 at high laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F6CSH either using BPS or CE. A Variable Predicted Importance MW (Da) (%) 44705 100.00 46584 88.26 13359 76.16 B Predicted Control 1 vs DF1_DHF1 Control 2 vs DF2_DHF2 MW (Da) p-value ROC value p-value ROC value 25402 0.00119 0.82885 0.00011 0.87222 44705 0.00002 0.06154 0.00040 0.15556 45584 0.00008 0.07885 0.00188 0.17778 117245  0.00715 0.21731 0.00537 0.23889 133359  0.00061 0.17692 0.00002 0.06667

TABLE 15

  (A) Variable importance of other potential splitter as predicted by BPS in the F6ISL (fraction 6 using IMAC at low laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F6ISL either using BPS or CE. The values highlighted in gray indicate their irrelevance as a potential biomarker at t1 of DENV infection, however, BPS analysis defined this peptide as a potential splitter.

TABLE 16 Table 16. (A) Variable importance of other potential splitter as predicted by BPS in the F6ISH (fraction 6 using IMAC at high laser intensity) fraction to discriminate between dengue and OFI at t1 (day of admission) and t2 (fever decreases to normal). (B) p-value and ROC value for all candidate biomarkers found in F6ISH either using BPS or CE. A Variable Predicted Importance MW (Da) (%) 13317 100.00 13181 60.63 B Predicted Control 1 vs DF1_DHF1 Control 2 vs DF2_DHF2 MW (Da) p-value ROC value p-value ROC value 11502 0.01192 0.75614 0.00049 0.83684 13181 0.00043 0.15614 0.00003 0.09298 13317 0.00000 0.00877 0.00001 0.05088 13400 0.00001 0.05088 0.00015 0.11404 133676  0.00049 0.17368 0.00003 0.07193

TABLE 17 Most significant biomarkers identified by SELDI technology and Biomarker Pattern Software (BPS) for detecting primary DENV infection at different stages of the disease. Primary Infection Biomarkers detected by SELDI and BPS (Ct vs DENV) Control 2 vs Control 1 vs DHF1 DHF2 Control 1 vs DF1 Control 2 vs DF2 p value roc p value roc p value roc p value roc m/z average F1CSL 0.00667 0.84074 0.19238 0.63942 0.00004 0.94444 0.01014 0.85043 3187.92612 0.00061 0.92963 0.02981 0.79327 0.00006 0.94444 0.00238 0.88462 3431.45742 0.00039 0.92963 0.01685 0.79327 0.00001 1.00000 0.00120 0.91880 3522.24286 0.00015 0.95926 0.00051 0.94712 0.00003 0.94444 0.00058 0.91880 3806.26212 0.00012 0.98889 0.00017 0.98558 0.00002 0.97222 0.00238 0.88462 3870.26222 0.00116 0.90000 0.00112 0.90865 0.00017 0.91667 0.00296 0.85043 3933.13794 0.61227 0.42593 0.00474 0.15385 0.49452 0.46111 0.01227 0.21795 3957.45555 0.57107 0.48519 0.01685 0.23077 1.00000 0.46111 0.00367 0.14957 3976.20723 0.00049 0.90000 0.00235 0.87019 0.00004 0.94444 0.00367 0.81624 4441.16417 0.00025 0.95926 0.02048 0.75481 0.00006 0.94444 0.00558 0.85043 4459.77765 0.00006 0.98889 0.00022 0.98558 0.00001 1.00000 0.00075 0.91880 4579.92629 0.00006 0.98889 0.00022 0.98558 0.00001 1.00000 0.00021 0.95299 4596.11099 0.00012 0.98889 0.00112 0.90865 0.20456 0.61111 0.05702 0.71368 4990.19603 0.00012 0.98889 0.00144 0.92308 0.00025 0.88889 0.04884 0.72222 6941.41838 0.00009 0.01111 0.00144 0.11538 0.00003 0.04444 0.00035 0.04701 7485.6467 F1CSH 0.02819 0.75833 0.34646 0.60096 0.00728 0.77778 0.06630 0.74786 10757.7961 0.00813 0.16667 0.02048 0.19231 0.02811 0.26667 0.05702 0.25214 11076.6199 0.00241 0.13333 0.16882 0.34615 0.01680 0.23889 0.01014 0.14957 13292.3331 0.00099 0.92083 0.00017 0.98558 0.00541 0.83333 0.00367 0.85043 23260.272 0.00156 0.88750 0.00022 0.98558 0.00248 0.86111 0.00835 0.81624 23823.3829 0.47768 0.55833 0.08219 0.69231 0.49452 0.42500 0.48320 0.44872 125373.713 F1ISL 0.00950 0.80357 0.12963 0.72857 0.00203 0.83929 0.00971 0.77778 3415.12898 0.02896 0.76786 0.30673 0.65238 0.00203 0.83929 0.09711 0.66667 3457.99405 0.04778 0.73214 0.08416 0.71905 0.05064 0.72024 0.00292 0.83333 3920.28814 0.00213 0.91071 0.97188 0.50000 0.01012 0.77976 0.03179 0.75000 4122.87062 0.63282 0.42857 0.00431 0.12857 0.57154 0.46429 0.01117 0.21111 4276.3415 0.49491 0.42857 0.00431 0.12857 0.50372 0.40476 0.00629 0.23889 4292.76446 0.00950 0.83929 0.19221 0.61429 0.00397 0.80952 0.00076 0.86111 4432.61693 0.00415 0.83929 0.50307 0.61429 0.00170 0.86905 0.00044 0.86111 4449.12593 0.04778 0.76786 0.00081 0.94762 0.75762 0.57143 0.40681 0.61111 4994.29386 0.05600 0.25000 0.04454 0.24286 0.07183 0.28571 0.07898 0.32222 6640.40179 0.01401 0.80357 0.00105 0.91905 0.00467 0.80952 0.00248 0.84167 6955.23285 F1ISH 0.01921 0.16667 0.00644 0.11111 0.00835 0.15000 0.00657 0.16667 10634.4592 0.03123 0.16667 0.00185 0.06667 0.46826 0.38333 0.31806 0.60000 12534.592 0.00145 0.95833 0.00046 0.98333 0.00835 0.80000 0.14924 0.66667 23104.0813 0.00200 0.95833 0.00108 0.98333 0.01222 0.80000 0.20202 0.60000 23638.655 0.03123 0.16667 0.01952 0.15556 0.00301 0.11667 0.03504 0.23333 56616.0019 F5CSL 0.11658 0.32051 0.05263 0.27778 0.62239 0.43007 0.14323 0.35000 6653.05308 0.02528 0.79060 0.22156 0.66296 0.00344 0.84965 0.00025 0.91667 8961.93991 0.01776 0.18376 0.00260 0.15926 0.03446 0.26224 0.00002 0.01667 12480.7656 0.00058 0.04701 0.00116 0.10000 0.00046 0.06643 0.00025 0.07222 12662.5319 0.02999 0.25214 0.42083 0.42593 0.00344 0.17832 0.30551 0.37778 44676.2104 F5CSH 0.04168 0.78205 0.10832 0.71429 0.00058 0.91880 0.41892 0.61111 10211.8483 0.02999 0.78205 0.03461 0.78571 0.00095 0.91880 0.56370 0.58333 10313.1139 0.48320 0.42308 0.61209 0.44048 0.04884 0.75641 0.72903 0.47222 10913.8933 0.01227 0.18376 0.79985 0.54762 0.05702 0.25214 0.90807 0.50000 12195.5553 0.00035 0.04701 0.00235 0.08333 0.00238 0.11538 0.00043 0.08333 12979.039 0.00035 0.04701 0.00177 0.08333 0.00190 0.11538 0.00003 0.00000 13092.8135 0.00009 0.01282 0.00072 0.01190 0.00035 0.04701 0.00003 0.00000 13295.3566 0.00016 0.01282 0.00177 0.08333 0.00151 0.11538 0.00007 0.02778 13325.8983 0.11658 0.68803 0.12819 0.26190 0.00684 0.85043 0.08326 0.72222 14029.4062 0.57030 0.42308 0.05191 0.22619 0.19286 0.66239 0.38648 0.63889 28370.513 0.86741 0.51709 0.00039 1.00000 0.92021 0.48291 0.00558 0.80556 108961.408 F5ISH 0.07857 0.30741 0.22156 0.36667 0.02811 0.72222 0.02480 0.26667 10226.9872 F6CSL 0.97622 0.48519 0.00062 0.92083 0.97930 0.49697 0.05591 0.71795 5289.43252 0.78845 0.57407 0.00062 0.95417 0.97930 0.52727 0.13436 0.67179 5474.34274 0.01127 0.18889 0.00671 0.16667 0.00240 0.13333 0.00060 0.11795 12481.1861 0.00049 0.10000 0.00125 0.10000 0.00005 0.04242 0.00008 0.09231 12650.5231 0.00039 0.07037 0.00099 0.06667 0.00098 0.13333 0.00012 0.06667 12906.1983 0.14404 0.33704 0.00049 0.06667 0.20353 0.37576 0.00030 0.11795 14429.2788 0.0112701 0.218518 0.00099 0.06667 0.00021 0.10303 0.00344 0.16923 45465.06 0.0295224 0.2481481 0.00368 0.13333 0.0020148 0.1636364 0.00344 0.19487 46196.85 F6CSH 0.15108 0.65385 0.01008 0.83333 0.54297 0.43007 0.81533 0.46667 10031.5232 0.11658 0.72222 0.00344 0.90952 0.83931 0.51399 0.93795 0.46667 10128.7882 0.00684 0.85043 0.00217 0.94762 0.00592 0.82168 0.00098 0.84848 25404.0365 0.00021 0.01282 0.01502 0.20476 0.00037 0.09441 0.00098 0.13333 44706.8965 0.00035 0.08120 0.00665 0.12857 0.00194 0.12238 0.01369 0.22424 45581.8839 0.05702 0.24359 0.91579 0.46190 0.01173 0.23427 0.01820 0.22424 46366.8165 0.97336 0.50855 0.86012 0.50000 0.00019 0.06643 0.00336 0.16364 59365.1146 0.44252 0.41453 0.75109 0.47143 0.00037 0.09441 0.00017 0.07273 117244.849 0.00190 0.11538 0.00081 0.05238 0.00706 0.17832 0.00025 0.07273 133359.809 0.02123 0.18376 0.00431 0.12857 0.07722 0.29021 0.00068 0.13333 198260.477 F6ISL 0.00556 0.15926 0.01401 0.20536 0.00128 0.12778 0.00467 0.22619 3437.48403 0.88150 0.51481 0.17224 0.27679 0.00842 0.80556 0.19849 0.63095 7625.50723 0.00316 0.15926 0.05600 0.28571 0.05704 0.29444 0.12282 0.34524 11724.8512 0.01127 0.15926 0.07597 0.28571 0.00842 0.21111 0.05064 0.28571 12478.098 0.01574 0.21852 0.00777 0.21429 0.02480 0.23889 0.00641 0.16667 34219.2306 F6ISH 0.00671 0.13333 0.00105 0.05238 0.00201 0.13333 0.00037 0.10000 13181.7725 0.00011 0.00000 0.00048 0.05238 0.00002 0.01212 0.00006 0.07222 13317.4205 0.00062 0.03333 0.00536 0.12857 0.00009 0.04242 0.00064 0.12778 13400.7181 0.27249 0.59583 0.80513 0.47143 0.00336 0.19394 0.00064 0.10000 59524.38 0.008132 0.1666667 0.00665 0.12857 0.00201 0.16364 0.00005 0.07222 133676.05

TABLE 18 Biomarkers identified by SELDI technology with a p-value smaller or equal to 0.05 that can discriminate primary DHF infection from OFI. Grouped according to fraction it was detected in. Diagnostic Ct1_2 vs DHF1_2 p value roc m/z averaqe F1CSL 0.0000000 0.9915966 4579.926292 0.0000000 0.9915966 4596.110987 0.0000001 0.9726891 5583.561565 0.0000001 0.9726891 3870.26222 0.0000002 0.9537815 3806.262116 0.0000003 0.9348739 4990.19603 0.0000004 0.9537815 6941.418376 0.0000006 0.0651261 7485.646702 0.0000025 0.8970588 3933.137941 0.0000025 0.8970588 4441.164166 0.0000078 0.8781513 4459.777653 0.0000078 0.8781513 4800.693027 0.0000097 0.8592437 3061.572119 0.0000120 0.8592437 3522.242859 0.0000133 0.8592437 4020.472527 0.0000165 0.8781513 6140.608328 0.0000183 0.8781513 6138.119764 0.0000204 0.8403361 4423.747371 0.0000278 0.8592437 4654.384151 0.0000342 0.8403361 5266.529592 0.0000925 0.8592437 3431.457425 0.0000925 0.8403361 5183.584315 0.0001234 0.1596639 7658.7026 0.0001975 0.8403361 4488.791713 0.0004078 0.8025210 2517.703609 0.0005789 0.8025210 2886.289725 0.0005789 0.8025210 23588.47849 0.0006875 0.8025210 3821.714797 0.0007487 0.8025210 3248.144023 0.0018483 0.2542017 2752.206092 0.0020004 0.2163866 2980.456404 0.0029479 0.2542017 10556.99254 0.0031808 0.7647059 3187.926121 0.0039834 0.7647059 6456.367141 0.0046163 0.2731092 7940.572626 0.0053390 0.7457983 9107.540827 0.0061624 0.7268908 8780.719118 0.0076127 0.7457983 38593.25709 0.0081601 0.7079832 5912.839452 0.0107190 0.7268908 4471.501882 0.0122483 0.2920168 3957.455551 0.0149050 0.7079832 4527.180907 0.0204696 0.6701681 2683.722459 0.0204696 0.7268908 3224.576562 0.0204696 0.6890756 37462.00218 0.0246148 0.6890756 6487.177441 0.0261495 0.3109244 4189.213856 0.0312595 0.3109244 3321.159048 0.0351234 0.3109244 3976.207226 0.0466040 0.3109244 7195.954395 F1CSH 0.0011751 0.8080357 10064.31699 0.0003352 0.8303571 10143.66604 0.0078297 0.7321429 10267.57388 0.0137340 0.6964286 10299.90259 0.0016480 0.7946429 10527.69159 0.0036925 0.7410714 10655.02359 0.0168046 0.7187500 10757.79609 0.0005816 0.8125000 10802.97632 0.0003680 0.1785714 11076.61987 0.0062852 0.2678571 11157.80715 0.0000302 0.1250000 11203.02954 0.0046532 0.7589286 11324.62766 0.0005313 0.1607143 11396.01715 0.0119729 0.7053571 11451.56989 0.0002777 0.1785714 11605.52007 0.0481324 0.3214286 12494.15007 0.0299026 0.3214286 12562.70923 0.0062852 0.2500000 12955.299 0.0005313 0.1607143 13292.33306 0.0072807 0.2678571 13419.85168 0.0004851 0.1785714 13474.4264 0.0005816 0.1785714 13841.62893 0.0157200 0.2678571 14022.97966 0.0001558 0.1785714 15094.27149 0.0097059 0.2857143 15308.55574 0.0000003 0.9642857 23260.27196 0.0000007 0.9464286 23823.38294 0.0004851 0.8214286 25774.83879 0.0067665 0.7589286 29110.79546 0.0017909 0.7589286 30257.11683 0.0146974 0.7232143 38507.26592 0.0157200 0.2857143 53621.70016 0.0072807 0.2678571 54009.77568 0.0050205 0.7366071 173467.5879 F1ISL 0.0111540 0.7114943 2716.553062 0.0490254 0.6517241 2862.774426 0.0014647 0.7804598 2923.355944 0.0057683 0.2287356 3277.946842 0.0017358 0.7804598 3415.128978 0.0220073 0.6931034 3457.994054 0.0462472 0.6517241 3501.960593 0.0004593 0.7988506 3793.065138 0.0077763 0.7252874 3920.288144 0.0206128 0.7114943 4122.870618 0.0168814 0.2839080 4276.341498 0.0103893 0.2471264 4292.764456 0.0364235 0.6517241 4414.952812 0.0042403 0.7482759 4432.616925 0.0137566 0.7114943 4449.125935 0.0000574 0.8724138 4994.293858 0.0010358 0.7850575 5272.160739 0.0077763 0.7344828 5606.87859 0.0490254 0.6655172 5908.422969 0.0049512 0.7574713 6127.388003 0.0250440 0.6977011 6455.01391 0.0111540 0.7344828 6488.439228 0.0284370 0.7022989 6508.732475 0.0284370 0.6931034 6588.428983 0.0053457 0.2471264 6640.401792 0.0000271 0.8586207 6955.232848 0.0234831 0.7114943 23565.20853 F1ISH 0.0385298 0.7145062 10294.4998 0.0243382 0.7191358 10308.54818 0.0176221 0.2700617 10495.53183 0.0357638 0.2962963 10587.75825 0.0005226 0.1620370 10614.34283 0.0003706 0.1450617 10634.45923 0.0034870 0.2098765 10649.30096 0.0009107 0.1666667 10687.34192 0.0331690 0.2746914 10715.49742 0.0067653 0.7577160 10832.80337 0.0074117 0.7361111 10965.73793 0.0115498 0.2314815 12051.19993 0.0034870 0.2314815 12135.8102 0.0002054 0.1234568 12534.59195 0.0067653 0.2314815 12582.46907 0.0162215 0.2314815 13073.75509 0.0385298 0.2962963 13927.30735 0.0000013 0.9691358 23104.0813 0.0000050 0.9475309 23638.655 0.0105871 0.7793210 46473.02215 0.0162215 0.7361111 47298.31195 0.0191276 0.2746914 50851.35303 0.0331690 0.2962963 51514.98459 0.0042320 0.2098765 53216.07485 0.0038431 0.2098765 53984.24055 0.0009107 0.2098765 54593.1773 0.0088733 0.2314815 55291.41693 0.0010150 0.1666667 56616.00188 0.0115498 0.2530864 69203.03801 0.0284597 0.2916667 75424.41325 0.0385298 0.2916667 125490.8119 0.0331690 0.2700617 135956.4468 F5CSL 0.0073978 0.2678571 2580.061762 0.0079102 0.7242063 2714.357253 0.0022059 0.2321429 3432.717673 0.0324998 0.3035714 4788.767558 0.0383881 0.3035714 4818.40977 0.0031934 0.2678571 5006.990761 0.0109798 0.2678571 6653.053079 0.0005280 0.1964286 7785.325256 0.0244017 0.6706349 8961.939907 0.0029685 0.2500000 9320.83479 0.0363319 0.6964286 9763.784856 0.0001187 0.2142857 12480.76558 0.0000023 0.1071429 12662.53194 0.0011908 0.7420635 41655.94365 0.0258646 0.3214286 45581.34627 0.0307173 0.3035714 46027.40418 0.0324998 0.3392857 47028.57811 F5CSH 0.0095299 0.7512500 10018.90389 0.0161569 0.7112500 10086.0305 0.0075263 0.7312500 10143.05116 0.0081477 0.7512500 10153.05258 0.0046118 0.7512500 10166.44834 0.0111176 0.6912500 10183.30263 0.0119962 0.7112500 10201.4022 0.0075263 0.7312500 10211.84828 0.0042405 0.7312500 10223.78504 0.0021156 0.7712500 10235.16667 0.0111176 0.7312500 10278.21222 0.0035781 0.7512500 10296.22207 0.0019337 0.7512500 10313.11386 0.0011118 0.7912500 10356.48068 0.0013406 0.7912500 10373.5117 0.0017663 0.7912500 10388.54465 0.0004633 0.8112500 10403.84952 0.0005654 0.8112500 10419.84632 0.0003419 0.8112500 10436.95581 0.0004190 0.8112500 10463.24082 0.0017663 0.7712500 10483.65617 0.0017663 0.7712500 10496.39967 0.0012213 0.7712500 10504.37612 0.0006882 0.7912500 10509.50161 0.0000854 0.8512500 10518.98678 0.0000190 0.8912500 10534.02712 0.0284126 0.6912500 10614.76076 0.0150125 0.7512500 10627.7903 0.0075263 0.7312500 10643.0144 0.0042405 0.7712500 10663.86727 0.0054440 0.7312500 10689.51826 0.0247685 0.6712500 10713.27982 0.0150125 0.7112500 10750.54704 0.0088147 0.2712500 11044.03097 0.0027595 0.2200000 11064.19634 0.0347411 0.3000000 11957.03925 0.0450209 0.3000000 12241.63856 0.0161569 0.2800000 12320.35659 0.0325094 0.3000000 12450.7298 0.0000386 0.1200000 12919.66629 0.0000033 0.0600000 12979.03902 0.0004633 0.1800000 13032.98071 0.0000010 0.0400000 13092.81349 0.0000117 0.0800000 13195.95886 0.0000002 0.0200000 13295.35656 0.0000008 0.0600000 13325.89827 0.0000055 0.0800000 13355.13127 0.0000764 0.1400000 13405.34327 0.0009196 0.2200000 13486.03676 0.0265365 0.2912500 14217.37567 0.0247685 0.3112500 14521.99131 0.0161569 0.2800000 14618.82347 0.0139401 0.2600000 14800.15747 0.0304018 0.7000000 17985.77938 0.0027595 0.7912500 18369.45605 0.0347411 0.7112500 33613.09559 0.0095299 0.7512500 33970.50674 0.0038965 0.7512500 34365.74762 0.0042405 0.7512500 34643.275 0.0231034 0.2800000 42755.36144 0.0325094 0.2800000 42989.41225 0.0247685 0.2600000 43408.65433 0.0032835 0.2400000 44690.90268 0.0027595 0.2600000 45327.80096 0.0075263 0.2400000 47174.35563 0.0119962 0.7312500 52881.10915 0.0075263 0.7712500 53767.53507 0.0265365 0.3200000 89686.37727 0.0347411 0.2800000 101489.63 0.0129359 0.7112500 108961.4085 0.0001643 0.1600000 118140.2057 0.0000854 0.1400000 134053.0066 0.0325094 0.2912500 151649.6484 0.0173773 0.3000000 168760.9312 0.0002508 0.1600000 199067.5944 F5ISH 0.0267757 0.3166667 10194.75241 0.0214507 0.3166667 10265.10592 0.0298421 0.3166667 10268.93612 0.0368884 0.3166667 10370.69947 0.0151936 0.3000000 10413.75186 0.0052747 0.2833333 10416.27302 0.0368884 0.3333333 10419.51807 0.0388567 0.3333333 10424.409 0.0099719 0.2666667 10454.03617 0.0476454 0.3277778 10753.95946 0.0010397 0.7833333 11814.91128 0.0191526 0.7166667 11926.05207 0.0127163 0.7166667 12162.99607 0.0368884 0.6888889 12771.26536 0.0388567 0.6888889 63202.19281 0.0282731 0.3111111 134528.9143 F6CSL 0.0394890 0.3078431 3357.303486 0.0013257 0.2019608 3428.881722 0.0253401 0.3078431 4126.549336 0.0064640 0.7598039 4409.265978 0.0056480 0.7245098 4868.735212 0.0108921 0.7245098 4966.887753 0.0416586 0.6892157 5026.966286 0.0239258 0.6960784 5070.710423 0.0283870 0.6784314 5098.522928 0.0108921 0.7313725 5260.926275 0.0300253 0.7068627 5289.432519 0.0253401 0.7068627 5366.242108 0.0189314 0.6960784 5390.905048 0.0178353 0.6784314 5474.342743 0.0108921 0.6784314 5502.673564 0.0178353 0.7068627 5557.327902 0.0178353 0.6960784 5594.085902 0.0148737 0.7137255 5612.225059 0.0189314 0.6892157 5656.80656 0.0213014 0.6960784 5713.615364 0.0189314 0.6960784 5733.192122 0.0439284 0.6892157 5779.276452 0.0283870 0.6784314 5831.045095 0.0200859 0.6960784 5913.554395 0.0463016 0.6539216 5946.280315 0.0213014 0.6715686 5967.465293 0.0374161 0.6892157 6005.550627 0.0268262 0.6715686 6032.27113 0.0283870 0.6715686 6049.453271 0.0189314 0.6892157 6097.371545 0.0213014 0.6715686 6117.220263 0.0374161 0.6892157 6138.640057 0.0102204 0.7598039 6186.818987 0.0089866 0.7137255 6208.87014 0.0374161 0.6892157 6269.981914 0.0317443 0.6715686 6291.763152 0.0158088 0.7068627 6368.819184 0.0283870 0.6784314 6405.544153 0.0022478 0.8019608 6489.894258 0.0102204 0.7313725 6505.638023 0.0335469 0.6715686 6545.783991 0.0463016 0.6892157 6777.027523 0.0148737 0.2480392 7784.588778 0.0002376 0.1882353 8237.401638 0.0253401 0.7176471 9047.708867 0.0416586 0.7000000 9960.034625 0.0084211 0.2725490 11573.82995 0.0123542 0.2725490 11751.31327 0.0000811 0.2019608 12481.18612 0.0000019 0.0960784 12650.52312 0.0000019 0.0960784 12906.19826 0.0416586 0.3078431 14103.42005 0.0003075 0.1843137 14429.27882 0.0463016 0.3186275 15184.98539 0.0064640 0.2549020 17435.05944 0.0189314 0.2901961 28157.11019 0.0317443 0.3078431 33514.17865 0.0084211 0.7352941 41482.3358 0.0024195 0.2549020 43439.78321 0.0014315 0.2019608 44236.21224 0.0003348 0.1843137 44629.01998 0.0000347 0.1490196 44938.74105 0.0000213 0.1490196 45465.05824 0.0002590 0.2019608 46196.8513 F6CSH 0.0046532 0.7500000 10031.5232 0.0029156 0.7723214 10128.78825 0.0218270 0.6875000 10197.87061 0.0168046 0.7053571 10244.13547 0.0015156 0.7767857 10283.30231 0.0358888 0.6875000 10376.97318 0.0072807 0.7410714 10461.31639 0.0380990 0.6875000 10503.04585 0.0179544 0.6875000 10764.84395 0.0024839 0.7723214 10769.30644 0.0404240 0.7053571 10804.20204 0.0157200 0.7053571 10851.39035 0.0358888 0.6696429 10909.85409 0.0264071 0.6830357 10978.99413 0.0026919 0.7544643 11049.12454 0.0002526 0.1785714 12717.12602 0.0006957 0.1785714 12888.24561 0.0247957 0.3035714 13164.31745 0.0004037 0.8035714 23169.52525 0.0017909 0.7946429 23418.55947 0.0015156 0.8035714 23683.93102 0.0001048 0.8258929 25404.03647 0.0004037 0.8258929 25984.38257 0.0058349 0.7232143 39174.72596 0.0005313 0.8080357 39939.38857 0.0000126 0.1250000 44706.89653 0.0000113 0.0892857 45581.88392 0.0000271 0.8571429 51329.3608 0.0000699 0.8437500 53487.79549 0.0078297 0.2500000 66724.50295 0.0013931 0.7723214 79341.48074 0.0004037 0.1964286 100935.9107 0.0000032 0.0714286 133359.809 0.0001279 0.1785714 198260.4773 F6ISL 0.0297570 0.6937120 2640.69294 0.0464532 0.6653144 2689.783828 0.0280872 0.6754564 2995.703282 0.0394441 0.6572008 3030.319866 0.0046075 0.7484787 3056.069215 0.0315115 0.6572008 3258.38603 0.0297570 0.3002028 3365.113073 0.0002376 0.1906694 3437.484027 0.0046075 0.2454361 7781.264988 0.0163556 0.7119675 8842.911218 0.0053075 0.2819473 11569.16318 0.0006149 0.2271805 11724.85116 0.0022072 0.2271805 12478.09797 0.0144293 0.3002028 12902.9216 0.0163556 0.2819473 33565.39821 0.0003097 0.1724138 34219.23059 F6ISH 0.0321237 0.6800000 10518.14292 0.0044957 0.7400000 10601.92366 0.0140543 0.7000000 10692.28071 0.0208103 0.7200000 10772.27076 0.0060548 0.7200000 10787.02725 0.0024156 0.7600000 10815.66745 0.0075274 0.7200000 10847.40644 0.0236200 0.7200000 10866.3702 0.0048470 0.7400000 10883.24318 0.0483445 0.6800000 10898.15904 0.0195181 0.7200000 10915.09457 0.0284502 0.6800000 10922.99947 0.0122789 0.7000000 10936.0038 0.0251444 0.6800000 10954.03205 0.0140543 0.7000000 10975.52556 0.0182965 0.7000000 10994.92546 0.0361964 0.6800000 11025.95351 0.0321237 0.7000000 11041.94186 0.0361964 0.6800000 11461.63754 0.0030615 0.7400000 11480.30167 0.0005261 0.8200000 11502.5506 0.0030615 0.7600000 11552.19581 0.0048470 0.7600000 11568.9046 0.0028305 0.7800000 11588.80348 0.0041677 0.7400000 11651.54813 0.0026155 0.7600000 11900.72691 0.0383928 0.7000000 11987.52615 0.0208103 0.3000000 12191.72667 0.0341081 0.2800000 12227.2304 0.0456728 0.3200000 12252.12747 0.0341081 0.3000000 12311.09499 0.0080853 0.2400000 12335.34416 0.0070042 0.2600000 12372.53121 0.0131401 0.2600000 12426.65893 0.0035759 0.2000000 12566.3271 0.0075274 0.2400000 12630.52717 0.0302390 0.3000000 12703.38429 0.0080853 0.2600000 12763.65458 0.0431265 0.3000000 12851.09203 0.0000203 0.1200000 13181.77247 0.0004393 0.2000000 13231.38136 0.0000002 0.0400000 13317.42046 0.0000118 0.1000000 13400.71809 0.0018967 0.2200000 13464.97455 0.0022297 0.2200000 13586.74139 0.0013635 0.1800000 13711.01396 0.0011524 0.2000000 13796.51085 0.0383928 0.6800000 23411.55 0.0052229 0.7400000 23915.54212 0.0004011 0.8400000 39529.93455 0.0000869 0.8400000 39888.64853 0.0140543 0.7400000 40571.4705 0.0052229 0.2200000 41956.29129 0.0016099 0.2600000 44526.33609 0.0002298 0.1800000 45322.88979 0.0361964 0.3400000 46419.54842 0.0208103 0.2800000 47393.37799 0.0002090 0.8200000 51260.79681 0.0002525 0.8200000 51799.54682 0.0006866 0.8000000 52580.37193 0.0080853 0.7400000 75206.21809 0.0010585 0.7800000 79541.53333 0.0000251 0.1200000 100624.8699 0.0001290 0.1600000 133676.0475

TABLE 19 Biomarkers identified by SELDI technology with a p-value smaller or equal to 0.05 that can discriminate primary DF infection from OFI. Grouped according to fraction it was detected in. Diagnostic Ct1_2 vs DF1_2 p value roc m/z averaqe F1CSL 0.0000000 0.9625850 4579.926292 0.0000000 0.9778912 4596.110987 0.0000004 0.9013605 5583.561565 0.0000001 0.9319728 3870.26222 0.0000000 0.9472789 3806.262116 0.0236516 0.6564626 4990.19603 0.0000448 0.8290816 6941.418376 0.0000001 0.0459184 7485.646702 0.0000011 0.9013605 3933.137941 0.0000004 0.9013605 4441.164166 0.0000008 0.9013605 4459.777653 0.0000140 0.8401361 4800.693027 0.0000005 0.9166667 3061.572119 0.0000000 0.9472789 3522.242859 0.0000128 0.8639456 6140.608328 0.0000411 0.8375850 6138.119764 0.0000097 0.8750000 4423.747371 0.0000184 0.8554422 4654.384151 0.0000007 0.9013605 3431.457425 0.0153351 0.6913265 5183.584315 0.0000014 0.1071429 7658.7026 0.0000154 0.8554422 4488.791713 0.0053031 0.7372449 2517.703609 0.0067852 0.7414966 2886.289725 0.0630724 0.6564626 23588.47849 0.0027893 0.7414966 3821.714797 0.0072096 0.7261905 3248.144023 0.0002988 0.1989796 2752.206092 0.0000106 0.1377551 2980.456404 0.0000011 0.9013605 3187.926121 0.0009909 0.2295918 7940.572626 0.0000128 0.8401361 9107.540827 0.0000965 0.8401361 8780.719118 0.0005509 0.7763605 5912.839452 0.0016234 0.7568027 4471.501882 0.0137100 0.2908163 3957.455551 0.0412997 0.3324830 4189.213856 0.0097100 0.2755102 3321.159048 0.0322319 0.3061224 3976.207226 0.0072096 0.2755102 7195.954395 0.0000184 0.8401361 3448.844444 0.0009220 0.2295918 2672.066808 0.0433518 0.3367347 8142.685699 0.0026103 0.7636054 4150.652833 0.0053031 0.2823129 3141.691497 0.0477149 0.3367347 4304.939937 0.0097100 0.7108844 3893.413384 0.0000489 0.8248299 4115.711469 0.0393306 0.3214286 3087.325152 0.0097100 0.3018707 2902.12753 0.0004070 0.7789116 10092.33514 F1CSH 0.0322319 0.3435374 10483.4761 0.0013168 0.7636054 10757.79609 0.0031814 0.2602041 11076.61987 0.0003769 0.2142857 11157.80715 0.0001140 0.1989796 11203.02954 0.0137100 0.3129252 11350.40235 0.0002988 0.1836735 11396.01715 0.0000168 0.1683673 11498.19671 0.0007411 0.2142857 11528.91453 0.0000097 0.1530612 11605.52007 0.0374418 0.3061224 11657.66336 0.0276559 0.3061224 12494.15007 0.0007974 0.2295918 13292.33306 0.0477149 0.3520408 13419.85168 0.0006394 0.2295918 13841.62893 0.0000448 0.8248299 23260.27196 0.0000533 0.8248299 23823.38294 0.0338949 0.6564626 25774.83879 0.0454892 0.6802721 29110.79546 0.0129560 0.6955782 30257.11683 0.0076577 0.7108844 38507.26592 0.0249266 0.6955782 46535.55302 0.0356307 0.3324830 53621.70016 0.0122390 0.3171769 54009.77568 0.0374418 0.3282313 57090.27251 0.0171275 0.3324830 63488.45467 0.0433518 0.3214286 149368.5056 F1ISL 0.0019931 0.7399425 2716.553062 0.0006862 0.7923851 2862.774426 0.0011457 0.7492816 2923.355944 0.0026829 0.7492816 3166.902131 0.0000396 0.8211207 3415.128978 0.0006428 0.7636494 3457.994054 0.0001752 0.7780172 3501.960593 0.0010097 0.7636494 3793.065138 0.0002672 0.7923851 3920.288144 0.0059277 0.7061782 4103.744956 0.0010757 0.7780172 4122.870618 0.0166483 0.7061782 4136.963402 0.0243579 0.3333333 4276.341498 0.0130015 0.2902299 4292.764456 0.0026829 0.7349138 4414.952812 0.0000073 0.8354885 4432.616925 0.0000028 0.8498563 4449.125935 0.0100801 0.7112069 4470.030305 0.0136685 0.7061782 5606.87859 0.0008335 0.7500000 5908.422969 0.0136685 0.6925287 5993.694087 0.0255115 0.6637931 6127.388003 0.0130015 0.6918103 6588.428983 0.0095715 0.3045977 6640.401792 0.0040169 0.3045977 6654.995846 0.0000102 0.8498563 6955.232848 0.0221857 0.3189655 8151.799057 0.0493518 0.3333333 41115.90512 F1ISH 0.0477569 0.3296296 10064.08045 0.0252391 0.2962963 10415.77202 0.0225628 0.3129630 10428.31602 0.0453879 0.3462963 10442.19383 0.0238684 0.2962963 10587.75825 0.0001075 0.1796296 10614.34283 0.0001525 0.1962963 10634.45923 0.0013461 0.2296296 10649.30096 0.0266773 0.3296296 10662.8102 0.0000477 0.1462963 10687.34192 0.0032007 0.2796296 10715.49742 0.0058850 0.2629630 10750.05013 0.0201368 0.2898148 10777.41795 0.0409462 0.3231481 10884.94807 0.0453879 0.3296296 11025.00949 0.0150417 0.3129630 11091.8077 0.0169247 0.2962963 11117.15405 0.0314243 0.6666667 12231.72039 0.0039363 0.7000000 23104.0813 0.0104530 0.7000000 23638.655 0.0213198 0.3129630 49665.31317 0.0169247 0.2962963 50851.35303 0.0067063 0.2796296 51514.98459 0.0003546 0.1962963 52248.13667 0.0012488 0.2129630 53216.07485 0.0003849 0.2129630 53984.24055 0.0001174 0.1629630 54593.1773 0.0004176 0.2129630 55291.41693 0.0002149 0.1962963 56616.00188 0.0331600 0.3296296 67189.1931 0.0081320 0.2962963 69203.03801 0.0297672 0.3296296 70488.37194 F5CSL 0.0447967 0.3268634 3360.320143 0.0009885 0.2336957 3432.717673 0.0255017 0.3268634 4788.767558 0.0011302 0.2336957 5006.990761 0.0447967 0.6459627 5165.803217 0.0080451 0.2802795 7785.325256 0.0076054 0.7150621 8726.282926 0.0000824 0.8167702 8846.247547 0.0000094 0.8633540 8961.939907 0.0002587 0.8012422 9192.684623 0.0003725 0.7701863 9468.993468 0.0004957 0.7701863 9679.241772 0.0000055 0.1560559 12480.76558 0.0000006 0.1094720 12662.53194 0.0294860 0.3423913 44310.14724 0.0080451 0.2647516 44676.21036 0.0067902 0.2958075 45036.99926 0.0131455 0.2958075 45285.08473 0.0085073 0.2802795 45581.34627 0.0111937 0.2958075 46027.40418 0.0100399 0.2958075 47028.57811 F5CSH 0.0001302 0.8152381 10018.90389 0.0026108 0.7466667 10086.0305 0.0037324 0.7466667 10143.05116 0.0028068 0.7638095 10153.05258 0.0034781 0.7638095 10166.44834 0.0052762 0.7466667 10183.30263 0.0056468 0.7295238 10201.4022 0.0020955 0.7466667 10211.84828 0.0011428 0.7638095 10223.78504 0.0012348 0.7638095 10235.16667 0.0188434 0.6952381 10278.21222 0.0131040 0.6952381 10296.22207 0.0060407 0.7295238 10313.11386 0.0052762 0.7123810 10356.48068 0.0108626 0.6952381 10373.5117 0.0095644 0.7123810 10388.54465 0.0016751 0.7809524 10403.84952 0.0042925 0.7123810 10419.84632 0.0056468 0.7295238 10436.95581 0.0167240 0.6780952 10463.24082 0.0131040 0.7123810 10483.65617 0.0095644 0.6952381 10496.39967 0.0123151 0.6952381 10504.37612 0.0049277 0.7123810 10509.50161 0.0018057 0.7638095 10518.98678 0.0069036 0.7123810 10534.02712 0.0593614 0.6438095 10614.76076 0.0095644 0.6952381 10627.7903 0.0131040 0.6609524 10643.0144 0.0237962 0.6780952 10663.86727 0.0224624 0.3085714 12130.19557 0.0157451 0.2742857 12810.96828 0.0000088 0.1200000 12919.66629 0.0000028 0.1028571 12979.03902 0.0000072 0.1542857 13032.98071 0.0000001 0.0514286 13092.81349 0.0000006 0.1028571 13195.95886 0.0000000 0.0342857 13295.35656 0.0000001 0.0514286 13325.89827 0.0000133 0.1371429 13355.13127 0.0000038 0.1200000 13405.34327 0.0000147 0.1200000 13486.03676 0.0037324 0.2571429 13633.86949 0.0009777 0.7809524 14029.40621 0.0019456 0.7638095 14996.39253 0.0484138 0.6857143 17665.44985 0.0089687 0.7295238 17790.31008 0.0004734 0.8152381 17985.77938 0.0000754 0.8323810 18369.45605 0.0459589 0.6514286 29222.15188 0.0046002 0.2571429 42755.36144 0.0064592 0.2742857 42989.41225 0.0030161 0.2571429 43408.65433 0.0010573 0.2228571 44690.90268 0.0022559 0.2228571 45327.80096 0.0089687 0.2914286 47174.35563 0.0056468 0.7123810 51440.05877 0.0069036 0.7200000 51889.38929 0.0049277 0.7295238 52881.10915 0.0078757 0.7295238 75141.88947 0.0237962 0.7123810 79071.92294 0.0157451 0.7123810 82425.00046 0.0237962 0.6780952 84062.87518 0.0028068 0.2400000 101489.63 0.0008348 0.2228571 118140.2057 0.0030161 0.2742857 134053.0066 0.0037324 0.2571429 168760.9312 0.0089687 0.3085714 199067.5944 F5ISL 0.0351749 0.3500000 43525.88014 F5ISH 0.0367139 0.3416667 11796.34403 0.0094934 0.3222222 13078.15809 0.0351749 0.3458333 13157.26855 0.0282801 0.3222222 13222.277 0.0148061 0.3222222 13254.13984 0.0050685 0.2944444 13274.84863 0.0170854 0.3138889 13538.51377 F6CSL 0.0032619 0.2527778 3357.303486 0.0000898 0.1930556 3428.881722 0.0104991 0.3041667 3473.520711 0.0141083 0.3180556 4126.549336 0.0077358 0.6944444 4178.147403 0.0007803 0.7583333 4409.265978 0.0134396 0.3041667 4788.788756 0.0270506 0.6833333 7636.624747 0.0270506 0.6666667 7661.987216 0.0062757 0.2944444 7784.588778 0.0023163 0.2527778 8237.401638 0.0032619 0.7166667 8296.864065 0.0472018 0.6708333 8722.157337 0.0247295 0.6986111 8746.621393 0.0008846 0.7638889 9047.708867 0.0491743 0.6569444 9463.07485 0.0322615 0.6847222 10472.88205 0.0029134 0.2805556 11573.82995 0.0015338 0.2527778 11751.31327 0.0000060 0.1277778 12481.18612 0.0000000 0.0444444 12650.52312 0.0000002 0.0861111 12906.19826 0.0003358 0.2111111 14429.27882 0.0434560 0.3083333 23566.3546 0.0434560 0.6652778 25601.84995 0.0012800 0.2208333 33514.17865 0.0014444 0.2625000 34088.06629 0.0014444 0.2347222 34523.55143 0.0036480 0.2805556 43439.78321 0.0010016 0.2527778 44236.21224 0.0000538 0.1555556 44629.01998 0.0000234 0.1694444 44938.74105 0.0000343 0.1833333 45465.05824 0.0000115 0.1694444 46196.8513 F6CSH 0.0162203 0.3214286 11147.59114 0.0286014 0.3214286 11670.74819 0.0069950 0.2889610 11750.95799 0.0021519 0.2240260 11780.11833 0.0017657 0.2402597 11832.56962 0.0035905 0.2564935 11908.94273 0.0078607 0.2889610 11993.02686 0.0420946 0.3214286 12057.41098 0.0008922 0.2727273 12717.12602 0.0005822 0.2402597 12888.24561 0.0110621 0.7110390 13612.48842 0.0051931 0.7272727 13846.52517 0.0002574 0.7759740 14825.26488 0.0002778 0.7759740 15055.63774 0.0078607 0.7110390 15411.25871 0.0029712 0.7272727 15613.40893 0.0009567 0.7435065 15812.1091 0.0015448 0.7272727 16257.83812 0.0029712 0.7272727 16334.75585 0.0026143 0.7435065 16782.20178 0.0022970 0.7272727 17134.49865 0.0145653 0.3051948 21786.35664 0.0000222 0.8409091 25404.03647 0.0002996 0.7759740 25984.38257 0.0331465 0.6948052 30482.78007 0.0162203 0.3051948 33560.43936 0.0016518 0.7597403 39939.38857 0.0074165 0.2564935 43596.91071 0.0000019 0.1103896 44706.89653 0.0000726 0.1915584 45581.88392 0.0005036 0.2240260 46366.8165 0.0021519 0.7435065 51329.3608 0.0258789 0.3214286 56681.92589 0.0004040 0.2240260 57685.14725 0.0000204 0.1590909 58847.42102 0.0000030 0.1266234 59365.11459 0.0000120 0.1590909 60097.13372 0.0001005 0.1915584 61383.37478 0.0008317 0.2402597 66724.50295 0.0365087 0.6623377 75362.49021 0.0401572 0.6298701 82507.09819 0.0029712 0.2727273 89782.65711 0.0001005 0.2077922 100935.9107 0.0000001 0.0779221 117244.8486 0.0000030 0.1428571 133359.809 0.0233833 0.3214286 149435.3445 0.0001751 0.2077922 198260.4773 F6ISL 0.0095715 0.2995690 2739.450599 0.0123635 0.3045977 3145.966693 0.0010757 0.2614943 3365.113073 0.0000102 0.1609195 3437.484027 0.0025296 0.7205460 7625.50723 0.0095715 0.2902299 7781.264988 0.0130015 0.6824713 8842.911218 0.0143656 0.2902299 11724.85116 0.0010097 0.2327586 12478.09797 0.0381955 0.3189655 12902.9216 0.0381955 0.6681034 15922.78821 0.0211642 0.6537356 16108.41436 0.0381955 0.3333333 33565.39821 0.0002019 0.2040230 34219.23059 F6ISH 0.0298954 0.6862319 10518.14292 0.0216133 0.6811594 10601.92366 0.0312772 0.6666667 10692.28071 0.0407710 0.6811594 10815.66745 0.0619867 0.6666667 10866.3702 0.0342060 0.6811594 10883.24318 0.0312772 0.6717391 10898.15904 0.0113934 0.6956522 10915.09457 0.0067313 0.6956522 10922.99947 0.0032489 0.7442029 10936.0038 0.0132657 0.6956522 10954.03205 0.0108237 0.7101449 10975.52556 0.0097596 0.7101449 10994.92546 0.0206106 0.6956522 11025.95351 0.0169945 0.6956522 11041.94186 0.0483741 0.6521739 11095.63804 0.0373659 0.6521739 11111.495 0.0012366 0.7536232 11461.63754 0.0048394 0.7101449 11480.30167 0.0003096 0.7826087 11502.5506 0.0014909 0.7536232 11535.71087 0.0030654 0.7536232 11552.19581 0.0206106 0.6956522 11568.9046 0.0206106 0.7101449 11588.80348 0.0011612 0.7731884 11900.72691 0.0357562 0.3340580 12191.72667 0.0407710 0.3340580 12227.2304 0.0260603 0.3050725 12252.12747 0.0425699 0.3340580 12311.09499 0.0119896 0.3195652 12335.34416 0.0030654 0.2326087 12372.53121 0.0154049 0.3340580 12426.65893 0.0051168 0.2760870 12566.3271 0.0067313 0.2760870 12630.52717 0.0444355 0.3340580 12660.70452 0.0327135 0.3340580 12703.38429 0.0951206 0.3579710 12763.65458 0.0169945 0.3195652 12816.16883 0.0000028 0.1166667 13181.77247 0.0000228 0.1601449 13231.38136 0.0000000 0.0297101 13317.42046 0.0000001 0.0876812 13400.71809 0.0005330 0.2181159 13464.97455 0.0028915 0.2471014 13586.74139 0.0001228 0.2036232 13711.01396 0.0001141 0.2181159 13796.51085 0.0373659 0.3485507 14550.43665 0.0216133 0.3340580 14599.05541 0.0139476 0.3050725 15028.22953 0.0048394 0.7297101 15772.51523 0.0040870 0.7391304 15807.50552 0.0051168 0.7152174 15961.34377 0.0022821 0.7297101 16106.21548 0.0000003 0.1021739 33558.37315 0.0006502 0.7876812 39529.93455 0.0019049 0.7246377 39888.64853 0.0285663 0.6717391 40571.4705 0.0021494 0.3050725 41956.29129 0.0146603 0.3195652 43536.29481 0.0000093 0.1456522 44526.33609 0.0000009 0.1311594 45322.88979 0.0015858 0.2615942 46419.54842 0.0079070 0.2855072 47393.37799 0.0057151 0.7152174 51260.79681 0.0060373 0.7152174 51799.54682 0.0272885 0.3340580 57741.52729 0.0000179 0.1746377 58960.28998 0.0000056 0.1456522 59524.3758 0.0002033 0.2181159 60636.58339 0.0002888 0.2181159 66625.27095 0.0113934 0.6956522 75206.21809 0.0000006 0.1166667 100624.8699 0.0000011 0.1311594 117726.354 0.0000003 0.1166667 133676.0475 0.0097596 0.2905797 149351.6364

TABLE 20 Biomarkers identified by SELDI technology with a p-value smaller or equal to 0.05 that can discriminate primary DF from primary DHF infection. Grouped according to fraction it was detected in. Prognostic DHF1_DHF2 vs DF1_DF2 p value roc m/z averaqe F1CSL 0.0038311 0.2436975 4990.19603 0.0014459 0.1904762 4020.472527 0.0211895 0.2829132 6138.119764 0.0031730 0.2436975 5266.529592 0.0413134 0.6890756 3431.457425 0.0443241 0.3081232 5183.584315 0.0413134 0.3081232 2752.206092 0.0142310 0.7282913 9107.540827 0.0007753 0.1904762 38593.25709 0.0266561 0.3025210 2683.722459 0.0211895 0.3025210 3224.576562 0.0072261 0.2633053 37462.00218 0.0167265 0.2689076 35401.0858 0.0384778 0.7086835 3448.844444 0.0102048 0.7478992 3685.41489 0.0111026 0.7282913 10287.01359 0.0131111 0.2829132 2902.12753 0.0004510 0.8263305 10092.33514 F1CSH 0.0039547 0.2425595 10527.69159 0.0344018 0.3050595 10802.97632 0.0017659 0.2008929 11324.62766 0.0399741 0.3258929 11350.40235 0.0024050 0.2217262 11451.56989 0.0043569 0.2500000 11498.19671 0.0014310 0.1800595 11528.91453 0.0462940 0.3258929 12013.23362 0.0214895 0.7351190 14022.97966 0.0295071 0.6726190 14346.75421 0.0252238 0.7142857 15094.27149 0.0232917 0.7008929 40033.21376 0.0430361 0.3467262 74862.91741 0.0232917 0.7008929 79154.5458 0.0318740 0.6875000 89630.65823 0.0318740 0.2916667 125373.7131 0.0252238 0.2842262 149368.5056 F1ISL 0.03038282 0.68055556 4432.616925 0.01657493 0.73888889 4449.125935 0.00937477 0.26111111 4994.293858 0.01937332 0.28055556 5272.160739 0.04040412 0.31944444 6455.01391 0.03038282 0.3 7167.316325 0.01657493 0.28472222 35469.71663 F1ISH 0.0292725 0.2583333 10129.14503 0.0114023 0.2583333 10147.96592 0.0114023 0.2500000 10777.41795 0.0030935 0.1833333 10832.80337 0.0141957 0.2333333 10965.73793 0.0158068 0.2333333 11025.00949 0.0216432 0.2833333 11057.71207 0.0018457 0.8000000 12135.8102 0.0064351 0.7750000 12231.72039 0.0001164 0.9000000 12534.59195 0.0030935 0.8250000 12582.46907 0.0355579 0.7250000 13927.30735 0.0039711 0.2083333 23104.0813 0.0391105 0.2833333 23638.655 0.0391105 0.7083333 79113.00591 F5CSL 0.0379534 0.3176329 2714.357253 0.0116705 0.7342995 5165.803217 0.0013507 0.7729469 8726.282926 0.0009327 0.7729469 8846.247547 0.0073716 0.7342995 9192.684623 0.0100387 0.7342995 9320.83479 0.0116705 0.7149758 9468.993468 0.0156545 0.7536232 9679.241772 0.0238679 0.7149758 15227.01519 0.0062929 0.7536232 28203.24796 0.0049399 0.7342995 28984.77362 0.0049399 0.2596618 41655.94365 0.0333455 0.3176329 43694.32147 F5CSH 0.0198099 0.6934524 10913.89329 0.0017659 0.7976190 14029.40621 0.0370989 0.6592262 14521.99131 0.0063629 0.7633929 14996.39253 0.0076496 0.7767857 15572.71556 0.0091649 0.7559524 15774.3005 0.0344018 0.7142857 17665.44985 0.0009298 0.8184524 28370.51303 0.0014310 0.8184524 29222.15188 0.0119414 0.2633929 53767.53507 0.0462940 0.6800595 84062.87518 F5ISH 0.04465415 0.65277778 10194.75241 0.03714679 0.6712963 10255.15564 0.04202078 0.68981481 10265.10592 0.03489597 0.68981481 10275.98212 0.03489597 0.68981481 10389.26482 0.0157542 0.72685185 10413.75186 0.0180921 0.72685185 10416.27302 0.04742539 0.6712963 10419.51807 0.030742 0.32638889 11124.07478 0.02369357 0.27083333 11796.34403 0.0180921 0.27777778 11926.05207 0.030742 0.31481481 12771.26536 0.003468 0.25925926 13423.48434 0.02701997 0.31481481 13888.42124 0.02369357 0.31481481 45360.95214 0.030742 0.2962963 47280.55103 F6CSL 0.0046335 0.81089744 4178.147403 0.00308916 0.25801282 4966.887753 0.04503826 0.30288462 6005.550627 0.04503826 0.34775641 6368.819184 0.03864703 0.30288462 6405.544153 0.0219851 0.28044872 6730.940725 0.0259475 0.28044872 6777.027523 0.03304353 0.30288462 6814.090876 0.00996754 0.22115385 6852.503653 0.00827638 0.25801282 6868.685109 0.04503826 0.32532051 6892.31088 0.03864703 0.28044872 6962.971613 0.04503826 0.32532051 7002.421072 0.04855452 0.32532051 7049.864742 0.04855452 0.32532051 7065.348357 0.01561114 0.30288462 7089.314476 0.01561114 0.75160256 7636.624747 0.02020929 0.72115385 7661.987216 0.03051287 0.70673077 8296.864065 0.00753117 0.72916667 15184.98539 0.00996754 0.72916667 15365.82604 0.02815052 0.28846154 23566.3546 F6CSH 0.0054498 0.2613636 10031.5232 0.0054498 0.2414773 10128.78825 0.0141298 0.2812500 10197.87061 0.0071351 0.2613636 10244.13547 0.0015587 0.2017045 10283.30231 0.0413476 0.3210227 10327.90565 0.0130094 0.3011364 10461.31639 0.0110017 0.2613636 10503.04585 0.0332771 0.2897727 10769.30644 0.0009285 0.1676136 10851.39035 0.0049737 0.2414773 10909.85409 0.0025639 0.2073864 11049.12454 0.0031109 0.2073864 11147.59114 0.0265943 0.3011364 11185.14578 0.0228130 0.2812500 11246.64624 0.0153345 0.2954545 11750.95799 0.0332771 0.3352273 11832.56962 0.0476029 0.3551136 11908.94273 0.0246409 0.2954545 11993.02686 0.0476029 0.3409091 12057.41098 0.0153345 0.2414773 12176.73044 0.0059668 0.7528409 13612.48842 0.0211040 0.7471591 13846.52517 0.0413476 0.6875000 14825.26488 0.0023248 0.7869318 15055.63774 0.0025639 0.7528409 15411.25871 0.0054498 0.7727273 15613.40893 0.0085049 0.7528409 15812.1091 0.0005418 0.1875000 23169.52525 0.0211040 0.3011364 23418.55947 0.0246409 0.3068182 23683.93102 0.0332771 0.3153409 43596.91071 0.0358029 0.3068182 51329.3608 0.0085049 0.2272727 53487.79549 0.0045355 0.2414773 57685.14725 0.0010315 0.2073864 58847.42102 0.0014076 0.2073864 59365.11459 0.0007504 0.1676136 60097.13372 0.0021063 0.2073864 61383.37478 0.0130094 0.2812500 91892.51895 0.0008350 0.1875000 117244.8486 F6ISL 0.0280624 0.3051471 2592.104434 0.0198735 0.2855392 2649.485504 0.0128643 0.2745098 2995.703282 0.0342578 0.3137255 3030.319866 0.0280624 0.2745098 3056.069215 0.0390083 0.2745098 4019.571278 0.0006409 0.8149510 7625.50723 0.0280624 0.7058824 15922.78821 0.0198735 0.7169118 16108.41436 F6ISH 0.0029637 0.2318841 14550.43665 0.0137479 0.2724638 14599.05541 0.0149363 0.7130435 15772.51523 0.0149363 0.7333333 15807.50552 0.0162143 0.7130435 15961.34377 0.0327382 0.6927536 16106.21548 0.0010750 0.1710145 23411.55 0.0006275 0.1768116 23915.54212 0.0106684 0.2782609 33558.37315 0.0116189 0.2782609 51260.79681 0.0260898 0.2724638 51799.54682 0.0106684 0.2376812 52580.37193 0.0470378 0.2927536 57741.52729 0.0002258 0.1768116 58960.28998 0.0000238 0.1159420 59524.3758 0.0001100 0.1565217 60636.58339 0.0190614 0.2724638 66625.27095 0.0149363 0.2579710 79541.53333 0.0019953 0.1768116 117726.354

TABLE 21 Most significant biomarkers identified by SELDI and BPS that can discriminate between secondary DF and secondary DHF infection at different stages of the disease. Grouped according to fraction it was found in. Secondary Infection p-value top 15% or < or = 0.05 AND ROC value < or = 0.25 or > or = 0.75 2DHF1 vs 2DHF2 vs 2DHF3 vs 2DF1 2DF2 2DF3 m/z Index p value roc p value roc p value roc average F1CSL 5 0.00671 0.23469 0.00113 0.18000 0.59628 0.55385 5268.88284 6 0.03867 0.26531 0.27169 0.36667 0.36904 0.57949 5513.34534 7 0.01310 0.21429 0.00953 0.23333 0.42016 0.60000 5559.47729 10 0.01688 0.23980 0.16468 0.36667 0.94491 0.47692 5675.03009 11 0.01688 0.23469 0.07118 0.34000 0.69539 0.57949 5688.54253 16 0.52005 0.56633 0.03620 0.68667 0.06882 0.68205 6887.48718 22 0.31209 0.38776 0.91741 0.52667 0.02000 0.75385 7434.41892 23 0.11824 0.64286 0.22110 0.63333 0.00461 0.17436 9314.81895 25 0.38266 0.61224 0.54755 0.55333 0.00344 0.20000 9526.36673 26 0.31209 0.63776 0.75574 0.55333 0.00532 0.20000 9550.87281 32 0.64589 0.46429 0.04006 0.28667 0.56474 0.43077 55807.6598 F1CSH 10 0.38266 0.40816 0.10134 0.31333 0.00397 0.19762 13462.0865 15 0.31209 0.38776 0.01074 0.23333 0.51269 0.43333 63122.4241 F1ISL 1 0.17765 0.66000 0.91741 0.50000 0.04881 0.74000 2565.21994 2 0.00844 0.23333 0.69355 0.58000 0.52028 0.60667 2614.7931 3 0.81955 0.55333 0.57551 0.58000 0.06493 0.71333 2656.55417 4 0.20584 0.63333 0.01708 0.74000 0.49373 0.44667 2842.30118 12 0.25402 0.60667 0.01910 0.71333 0.32969 0.39333 3485.24531 19 0.02379 0.76667 0.06493 0.71333 0.14089 0.36667 9298.45491 22 0.01209 0.76667 0.78746 0.47333 0.75574 0.47333 44956.201 F1ISH 1 0.10930 0.65385 0.27014 0.61735 0.79999 0.47692 10323.6689 2 0.03275 0.74451 0.35812 0.61224 0.11200 0.68205 10911.3918 3 1.00000 0.52473 0.04818 0.71429 0.53402 0.55385 10967.399 5 0.92268 0.50549 0.02742 0.71429 0.24013 0.63077 11308.5233 6 0.26438 0.38736 0.00671 0.81122 0.79999 0.48205 11468.1974 9 0.80829 0.55220 0.29060 0.63776 0.00461 0.83077 13486.9672 13 0.33179 0.59890 0.33459 0.61224 0.27901 0.58462 39896.2782 15 0.05225 0.70879 0.29060 0.61224 0.53402 0.58462 43960.757 14 0.01985 0.76374 0.35812 0.59184 0.79999 0.53333 42259.3628 16 0.04664 0.70879 0.49069 0.56122 0.94491 0.53333 44577.8155 21 0.92268 0.49725 0.01688 0.73980 0.39410 0.58462 88302.0484 24 0.46668 0.57143 0.49069 0.56122 0.56474 0.54872 111262.512 F5CSL 1 0.78279 0.51531 0.57551 0.52667 0.09870 0.68182 5077.61334 2 0.81830 0.46429 0.02944 0.68667 0.31064 0.40210 6658.89951 4 0.52005 0.44388 0.00953 0.74000 0.33910 0.37413 6688.97411 5 0.64589 0.43878 0.17765 0.63333 0.02981 0.76573 6826.12668 9 0.71319 0.54082 0.22110 0.63333 0.01380 0.20629 8845.3908 10 0.43474 0.61224 0.06493 0.66000 0.01380 0.17832 8964.59038 11 0.46224 0.58673 0.07793 0.71333 0.00839 0.17832 8982.52189 13 0.55029 0.56122 0.17765 0.66000 0.00496 0.17832 9316.3678 14 0.71319 0.56122 0.37251 0.58000 0.00706 0.20629 9469.87316 17 0.06608 0.70918 0.07793 0.71333 0.66391 0.43007 11760.7821 21 0.00380 0.81122 0.88457 0.44667 0.66391 0.45804 25640.9662 24 0.25068 0.60714 0.30953 0.58000 0.02981 0.23427 53647.346 25 0.64589 0.53571 0.88457 0.52667 0.05227 0.26224 67060.0819 26 0.96335 0.46429 0.11029 0.66000 0.00285 0.15035 75331.1654 F5CSH 4 0.16586 0.63889 0.52815 0.52473 0.00526 0.82231 12378.5059 6 0.03266 0.75000 0.22507 0.59890 0.62237 0.53306 12606.1744 F6CSL 1 0.51269 0.58810 0.04024 0.31429 0.96519 0.49524 4198.06271 2 0.11614 0.33333 0.07355 0.29048 0.79343 0.49524 5162.57462 3 0.03247 0.70714 0.51269 0.41190 0.31547 0.61190 6952.81298 4 0.04469 0.72857 0.07355 0.66905 0.51269 0.56429 11719.8723 5 0.01638 0.75238 0.60047 0.55238 0.04469 0.70476 11917.5926 6 0.04469 0.73095 0.07355 0.71667 0.08874 0.66190 12913.932 7 0.13784 0.63810 0.03618 0.71429 0.22170 0.61190 46601.9329 8 0.31547 0.61190 0.45812 0.57619 0.79343 0.47143 54417.1306 F6CSH 2 0.82726 0.51905 0.80829 0.51648 0.23865 0.37857 11783.4664 4 0.02324 0.72857 0.14545 0.66209 0.69447 0.44524 12436.087 5 0.03618 0.72857 0.10930 0.63462 0.96519 0.47143 12569.9265 8 0.03618 0.28571 0.12046 0.28571 0.75998 0.56429 32267.4992 10 0.31547 0.63571 0.26438 0.66209 0.57047 0.42857 39867.5323 12 0.79343 0.54524 0.01985 0.76374 0.17607 0.61429 46697.2707 14 0.00048 0.11905 0.88425 0.46978 0.93044 0.49524 150108.44 F6ISH 3 0.00040 0.86224 0.00394 0.79333 0.72442 0.47333 13533.1707 4 0.00282 0.81122 0.17765 0.66000 0.54755 0.55333 13816.9437 13 0.00770 0.21429 0.98345 0.52667 0.60413 0.47333 49749.6447 *No data for fractions F5ISL, F5ISH and F6ISL

TABLE 22 Biomarkers detected by SELDI technology with a p-value smaller or equal to 0.05 that can discriminate secondary DENV infection from OFI. Grouped according to fraction it was found in. Ct1_2 vs 2DF1_2 Ct1_2 vs 2DHF1_2 p-value roc value m/z average p-value roc value m/z average F1CSL 0.0000000 0.0151515 2625.181487 F1CSL 0.0000000 0.0454545 2625.181487 0.0000000 0.0303030 2667.845783 0.0000000 0.0454545 2667.845783 0.0000000 0.0151515 2741.725854 0.0000000 0.0303030 2741.725854 0.0000000 0.0606061 2856.727191 0.0000001 0.0757576 2856.727191 0.0000000 0.0151515 2872.154934 0.0000000 0.0303030 2872.154934 0.0001261 0.1969697 2897.527715 0.0001169 0.1969697 2897.527715 0.0000000 0.0454545 2921.681011 0.0000004 0.0909091 2921.681011 0.0000334 0.1666667 2938.078868 0.0000461 0.1818182 2938.078868 0.0000000 0.0454545 2990.831829 0.0000000 0.0303030 2990.831829 0.0000000 0.0303030 3043.041845 0.0000002 0.0757576 3043.041845 0.0000001 0.0606061 3071.048466 0.0000008 0.1303030 3071.048466 0.0000000 0.0303030 3145.264181 0.0000004 0.1060606 3145.264181 0.0000000 0.0151515 3175.340726 0.0000000 0.0757576 3175.340726 0.0000000 0.0303030 3209.081505 0.0000000 0.0757576 3209.081505 0.0000000 0.0151515 3262.112346 0.0000000 0.0606061 3262.112346 0.0000000 0.0000000 3280.760876 0.0000000 0.0151515 3280.760876 0.0000000 0.0606061 3307.659966 0.0000001 0.0757576 3307.659966 0.0000000 0.0303030 3358.521584 0.0000052 0.1303030 3358.521584 0.0000005 0.0848485 3420.037403 0.0002280 0.1848485 3420.037403 0.0000000 0.0151515 3437.42944 0.0000009 0.1363636 3437.42944 0.0000001 0.0757576 3511.538012 0.0000012 0.1212121 3511.538012 0.0000000 0.0151515 3589.060244 0.0000000 0.0454545 3589.060244 0.0000001 0.0757576 3631.061336 0.0001084 0.2000000 3631.061336 0.0000000 0.0151515 3680.115892 0.0000000 0.0303030 3680.115892 0.0001970 0.1969697 3799.639473 0.0250180 0.2969697 3799.639473 0.0000000 0.0151515 3814.303638 0.0000000 0.0757576 3814.303638 0.0000003 0.0909091 3863.107595 0.0018605 0.2363636 3863.107595 0.0000040 0.1363636 3884.602302 0.0000461 0.1606061 3884.602302 0.0064753 0.2818182 3923.916704 0.0000008 0.1060606 3949.327603 0.0000000 0.0606061 3949.327603 0.0000023 0.1363636 3965.762902 0.0000001 0.0606061 3965.762902 0.0000000 0.0303030 4063.241745 0.0000000 0.0151515 4063.241745 0.0051615 0.2757576 4103.551305 0.0000683 0.1848485 4103.551305 0.0117705 0.2818182 4128.656395 0.0001169 0.2060606 4128.656395 0.0003042 0.2272727 4143.916106 0.0000052 0.1363636 4143.916106 0.0000001 0.0757576 4182.768879 0.0000000 0.0454545 4182.768879 0.0000016 0.1212121 4279.033423 0.0000000 0.0454545 4279.033423 0.0000003 0.0909091 4299.724384 0.0000000 0.0606061 4299.724384 0.0001578 0.1818182 4468.900331 0.0454632 0.3515152 4450.645147 0.0250180 0.3272727 4492.301874 0.0000114 0.1666667 4468.900331 0.0000009 0.1060606 4524.063737 0.0007491 0.2515152 4492.301874 0.0000062 0.8666667 4573.192933 0.0000000 0.0303030 4524.063737 0.0000019 0.8818182 4588.379469 0.0001084 0.8060606 4573.192933 0.0117705 0.3121212 4646.271132 0.0000334 0.8363636 4588.379469 0.0003266 0.7909091 4972.46769 0.0017471 0.2363636 4646.271132 0.0000008 0.8969697 4985.052133 0.0160481 0.7000000 4972.46769 0.0000062 0.8666667 4995.082985 0.0001700 0.8212121 4985.052133 0.0000930 0.8060606 5487.254589 0.0007491 0.7606061 4995.082985 0.0000000 0.9727273 5559.119903 0.0006544 0.7606061 5487.254589 0.0000000 0.9727273 5574.710692 0.0000000 0.9575758 5559.119903 0.0000003 0.8969697 5590.063516 0.0000000 0.9424242 5574.710692 0.0000000 0.9878788 5675.016336 0.0000048 0.8727273 5590.063516 0.0000000 0.9878788 5689.765089 0.0000000 0.9727273 5675.016336 0.0474992 0.6454545 5767.809439 0.0000000 0.9727273 5689.765089 0.0001700 0.8030303 5912.163434 0.0000584 0.8181818 5912.163434 0.0004642 0.7606061 5996.460394 0.0000135 0.8424242 5996.460394 0.0057849 0.7303030 6013.779325 0.0025358 0.7151515 6013.779325 0.0000030 0.8666667 6127.503716 0.0000004 0.8969697 6127.503716 0.0000000 0.9424242 6143.838117 0.0000000 0.9575758 6143.838117 0.0038600 0.7454545 6454.996063 0.0000088 0.8363636 6454.996063 0.0000028 0.8666667 6487.509564 0.0000000 0.9424242 6487.509564 0.0000004 0.9121212 6591.475901 0.0000000 0.9575758 6591.475901 0.0454632 0.6454545 6642.439305 0.0005710 0.7606061 6642.439305 0.0117705 0.6848485 6652.107044 0.0000204 0.8212121 6652.107044 0.0000241 0.8363636 6683.82645 0.0000002 0.8969697 6683.82645 0.0000930 0.8060606 6805.698908 0.0000002 0.9121212 6805.698908 0.0007003 0.7757576 6860.453871 0.0000011 0.8969697 6860.453871 0.0000000 0.9575758 6944.325104 0.0000000 0.9878788 6944.325104 0.0002831 0.7757576 7160.085957 0.0000683 0.8212121 7160.085957 0.0001084 0.8060606 7331.96616 0.0000096 0.8363636 7331.96616 0.0111656 0.7151515 7350.027102 0.0160481 0.7000000 7350.027102 0.0000135 0.8515152 7415.948421 0.0000114 0.8666667 7415.948421 0.0011899 0.7454545 7589.702883 0.0006544 0.7757576 7589.702883 0.0380394 0.6606061 7661.734977 0.0000006 0.1212121 8150.546744 0.0000007 0.1060606 8150.546744 0.0036386 0.2727273 8314.622635 0.0137632 0.3121212 8314.622635 0.0000135 0.1666667 8356.79503 0.0000683 0.1969697 8356.79503 0.0000005 0.1060606 8459.519265 0.0000036 0.1363636 8459.519265 0.0004330 0.2121212 8625.552866 0.0168806 0.2969697 8625.552866 0.0015391 0.2363636 8954.752999 0.0013541 0.2363636 8954.752999 0.0022425 0.2454545 9170.910437 0.0015391 0.2303030 9170.910437 0.0000004 0.9121212 10101.46975 0.0000146 0.8515152 10101.46975 0.0012696 0.7666667 10289.18681 0.0022425 0.7363636 10289.18681 0.0000135 0.8363636 11135.15931 0.0000425 0.8060606 11135.15931 0.0000159 0.8515152 11491.70855 0.0000540 0.8060606 11491.70855 0.0000284 0.8363636 11677.83181 0.0004037 0.7969697 11677.83181 0.0000008 0.8818182 11743.17361 0.0000044 0.8666667 11743.17361 0.0000002 0.9272727 11902.07203 0.0000001 0.9272727 11902.07203 0.0000006 0.9121212 12083.60453 0.0000001 0.9121212 12083.60453 0.0000632 0.8363636 12590.76335 0.0032301 0.7303030 12590.76335 0.0028638 0.7606061 12896.80544 0.0009152 0.7606061 13115.34777 0.0000021 0.8818182 13115.34777 0.0000000 0.9727273 13375.41938 0.0000000 0.9727273 13375.41938 0.0000003 0.9272727 14757.7248 0.0000096 0.8666667 14757.7248 0.0000006 0.9121212 15198.38517 0.0000002 0.9272727 15198.38517 0.0000334 0.8060606 15906.99196 0.0000146 0.8515152 15906.99196 0.0006114 0.7757576 16080.83999 0.0001578 0.7909091 16080.83999 0.0000000 0.9424242 22937.26034 0.0000000 0.9424242 22937.26034 0.0000000 0.9424242 23544.76704 0.0000000 0.9575758 23544.76704 0.0000011 0.8969697 25497.91473 0.0000013 0.8818182 25497.91473 0.0000461 0.1606061 35405.0346 0.0000104 0.1454545 35405.0346 0.0000081 0.8666667 37705.72097 0.0000284 0.8212121 37705.72097 0.0000001 0.9121212 44810.68881 0.0000000 0.9575758 44810.68881 0.0000003 0.9121212 45184.10294 0.0000000 0.9575758 45184.10294 0.0000006 0.8969697 45616.26741 0.0000000 0.9575758 45616.26741 0.0000001 0.9272727 46357.92645 0.0000000 0.9575758 46357.92645 F1CSH 0.0005411 0.7533333 10025.87122 F1CSH 0.0194944 0.6866667 10025.87122 0.0000004 0.8866667 10130.28134 0.0000061 0.8333333 10130.28134 0.0000002 0.8866667 10149.11798 0.0000037 0.8466667 10149.11798 0.0000006 0.8866667 10171.11128 0.0000113 0.8066667 10171.11128 0.0000057 0.8466667 10196.44234 0.0000743 0.7933333 10196.44234 0.0000070 0.8466667 10215.38716 0.0001286 0.7933333 10215.38716 0.0000022 0.8600000 10224.52891 0.0000286 0.8200000 10224.52891 0.0000003 0.8733333 10241.85866 0.0000046 0.8333333 10241.85866 0.0000001 0.8733333 10297.3244 0.0000019 0.8466667 10297.3244 0.0000002 0.8866667 10351.51024 0.0000011 0.8466667 10351.51024 0.0000001 0.8733333 10369.2482 0.0000009 0.8600000 10369.2482 0.0000001 0.9000000 10413.34138 0.0000019 0.8466667 10413.34138 0.0000001 0.8866667 10444.68912 0.0000040 0.8466667 10444.68912 0.0000001 0.9000000 10461.79685 0.0000026 0.8466667 10461.79685 0.0000002 0.8733333 10481.71491 0.0000037 0.8333333 10481.71491 0.0000001 0.9000000 10490.47668 0.0000005 0.8466667 10490.47668 0.0000001 0.9000000 10496.60495 0.0000018 0.8600000 10496.60495 0.0000001 0.8866667 10507.40716 0.0000007 0.8600000 10507.40716 0.0000001 0.9000000 10514.61735 0.0000008 0.8600000 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10790.91972 0.0000002 0.9025765 10665.17962 0.0074402 0.7254428 10816.55733 0.0000021 0.8864734 10672.3618 0.0001789 0.7898551 10874.11936 0.0000012 0.8864734 10681.084 0.0012752 0.7737520 10891.02957 0.0000012 0.9025765 10694.26283 0.0004110 0.7898551 10906.58841 0.0000104 0.8381643 10711.81013 0.0009691 0.7737520 10927.60682 0.0002433 0.7898551 10725.60891 0.0180288 0.7254428 10948.05849 0.0000809 0.8059581 10736.52558 0.0381678 0.6449275 11021.63296 0.0003053 0.7898551 10751.55877 0.0145703 0.7093398 11095.98577 0.0000494 0.8381643 10773.15923 0.0023167 0.7093398 11115.98954 0.0004758 0.7737520 10790.91972 0.0145703 0.7093398 11132.56083 0.0001531 0.8059581 10874.11936 0.0023167 0.2423510 11187.64311 0.0020339 0.7415459 10891.02957 0.0004758 0.1940419 11197.80359 0.0009040 0.7737520 10906.58841 0.0162187 0.3067633 11283.65755 0.0110785 0.6932367 10927.60682 0.0007858 0.2423510 11394.7004 0.0162187 0.6932367 10948.05849 0.0046230 0.2745572 11884.93051 0.0078838 0.6932367 11021.63296 0.0000298 0.1618357 11917.82491 0.0233475 0.6771337 11076.12773 0.0000809 0.1779388 11953.73243 0.0003290 0.8059581 11095.98577 0.0000384 0.1618357 11976.91805 0.0017831 0.7737520 11115.98954 0.0000012 0.1135266 11996.5121 0.0439411 0.6610306 11132.56083 0.0000004 0.0813205 12027.11641 0.0110785 0.3067633 11187.64311 0.0000003 0.0652174 12052.53166 0.0285359 0.3550725 11197.80359 0.0000494 0.1940419 12084.07622 0.0346883 0.3389694 11210.19746 0.0000079 0.1296296 12925.60447 0.0271535 0.3389694 11283.65755 0.0000013 0.1457327 12974.23339 0.0000633 0.1618357 11394.7004 0.0000004 0.0974235 13028.74639 0.0000114 0.1457327 11884.93051 0.0000000 0.0491143 13065.70027 0.0000162 0.1457327 11917.82491 0.0000000 0.0652174 13099.66919 0.0000633 0.1779388 11953.73243 0.0000066 0.1296296 13146.65925 0.0000537 0.1618357 11976.91805 0.0000003 0.0813205 13187.45517 0.0000003 0.0974235 11996.5121 0.0000009 0.0974235 13233.79106 0.0000015 0.1296296 12027.11641 0.0000002 0.0813205 13291.70681 0.0000009 0.0974235 12052.53166 0.0000002 0.0652174 13320.77403 0.0000298 0.1779388 12084.07622 0.0000001 0.0652174 13356.70436 0.0138029 0.6932367 12493.00409 0.0000230 0.1618357 13426.83767 0.0088427 0.7093398 12511.09369 0.0003817 0.2101449 13491.55847 0.0000148 0.1457327 12925.60447 0.0299785 0.3550725 13581.27869 0.0000006 0.0974235 12974.23339 0.0189985 0.3389694 13655.43547 0.0000010 0.1135266 13028.74639 0.0000087 0.1296296 14019.70586 0.0000000 0.0330113 13065.70027 0.0000000 0.0330113 14214.24986 0.0000000 0.0330113 13099.66919 0.0000000 0.0169082 14344.33114 0.0000021 0.1135266 13146.65925 0.0016686 0.7576490 14729.54802 0.0000001 0.0652174 13187.45517 0.0058812 0.7254428 14767.85965 0.0000000 0.0169082 13233.79106 0.0000005 0.1135266 15566.82603 0.0000000 0.0330113 13291.70681 0.0000353 0.1618357 15645.08235 0.0000002 0.0813205 13320.77403 0.0070191 0.2906602 16290.00891 0.0000000 0.0491143 13356.70436 0.0000000 0.0491143 17653.98849 0.0000007 0.0974235 13426.83767 0.0000015 0.1135266 17809.61765 0.0001116 0.1940419 13491.55847 0.0000011 0.8864734 17974.36692 0.0043492 0.2906602 13581.27869 0.0000746 0.8059581 29272.79419 0.0000001 0.0813205 14019.70586 0.0004758 0.7737520 33625.51714 0.0000000 0.0169082 14214.24986 0.0004423 0.7737520 33961.79795 0.0000000 0.0169082 14344.33114 0.0006821 0.2262480 42641.96081 0.0000136 0.8703704 14729.54802 0.0003290 0.2101449 42923.06964 0.0000251 0.8220612 14767.85965 0.0000951 0.2101449 43153.71795 0.0093602 0.6932367 14855.80746 0.0000537 0.1940419 43558.60561 0.0000015 0.1296296 15566.82603 0.0000072 0.1135266 44673.19334 0.0000079 0.1457327 15645.08235 0.0000001 0.0652174 45254.93201 0.0033949 0.2906602 16290.00891 0.0028089 0.7254428 60596.55945 0.0000000 0.0491143 17653.98849 0.0000011 0.9025765 61369.70702 0.0000273 0.1618357 17809.61765 0.0023167 0.2745572 66754.36307 0.0000124 0.8703704 17974.36692 0.0040902 0.2584541 75408.4976 0.0020339 0.7576490 26021.04631 0.0010385 0.2423510 79145.35138 0.0000019 0.8864734 29272.79419 0.0020339 0.2584541 91493.20762 0.0110785 0.7093398 33625.51714 0.0000324 0.1779388 133992.2232 0.0145703 0.7093398 33961.79795 0.0110785 0.2584541 168584.5002 0.0001531 0.2101449 42641.96081 0.0000124 0.1457327 198991.3517 0.0001655 0.1940419 42923.06964 F6CSL 0.0000046 0.8403576 2990.018687 0.0000211 0.1618357 43153.71795 0.0000168 0.8122605 3360.866902 0.0000273 0.1779388 43558.60561 0.0000050 0.8263091 4179.401548 0.0000005 0.1135266 44673.19334 0.0000022 0.8544061 4198.411686 0.0000001 0.0813205 45254.93201 0.0000016 0.8684547 4252.48155 0.0200134 0.6610306 60596.55945 0.0000260 0.8122605 4354.864569 0.0000177 0.8381643 61369.70702 0.0000007 0.8544061 4410.72305 0.0099045 0.2584541 66754.36307 0.0000750 0.7982120 4466.484268 0.0271535 0.3067633 75408.4976 0.0000860 0.7841635 4536.675037 0.0002831 0.2101449 79145.35138 0.0002040 0.7560664 4626.037492 0.0005117 0.2423510 91493.20762 0.0030793 0.7279693 4818.821494 0.0007322 0.1940419 133992.2232 0.0231454 0.3346105 5882.598843 0.0016686 0.2262480 168584.5002 0.0000022 0.8544061 6487.935491 0.0000136 0.1618357 198991.3517 0.0027670 0.7279693 6544.667343 F6CSL 0.0000001 0.8965517 2990.018687 0.0000019 0.8684547 6689.164478 0.0000016 0.8544061 3360.866902 0.0351130 0.6436782 6855.504433 0.0001678 0.7701149 4179.401548 0.0123987 0.6858238 6953.588087 0.0001376 0.7701149 4198.411686 0.0129846 0.6998723 7588.918733 0.0000019 0.8825032 4252.48155 0.0252060 0.6717752 8719.481654 0.0018881 0.7139208 4354.864569 0.0000099 0.8263091 8842.966359 0.0000002 0.8965517 4410.72305 0.0000039 0.8544061 9046.727666 0.0000347 0.8263091 4466.484268 0.0029193 0.7139208 9165.408341 0.0002639 0.7841635 4536.675037 0.0021086 0.7420179 9668.960682 0.0011345 0.7560664 4626.037492 0.0000115 0.1660281 10294.7238 0.0098066 0.6922095 4818.821494 0.0030793 0.2784163 10463.66285 0.0186220 0.3128991 5014.908804 0.0001288 0.2081737 10733.07419 0.0252060 0.3269476 5017.339281 0.0000001 0.0881226 12480.3404 0.0023525 0.2848020 5882.598843 0.0000000 0.0600255 12651.6673 0.0000000 0.8965517 6487.935491 0.0463412 0.6781609 13920.46348 0.0004932 0.7560664 6544.667343 0.0262945 0.6922095 14102.40615 0.0118364 0.6858238 6645.390397 0.0027670 0.7279693 15178.11257 0.0000000 0.9386973 6689.164478 0.0007091 0.7560664 15361.4929 0.0026219 0.7279693 6855.504433 0.0001912 0.7841635 15915.33173 0.0014263 0.7420179 6871.100138 0.0001471 0.7841635 16093.36476 0.0030793 0.7139208 6953.588087 0.0019956 0.7420179 17426.66866 0.0274235 0.3128991 7781.319356 0.0000429 0.7982120 18048.90648 0.0012017 0.7279693 8606.881674 0.0005243 0.2286079 23037.50948 0.0001571 0.7701149 8719.481654 0.0162966 0.3269476 23524.56381 0.0000000 0.9386973 8842.966359 0.0118364 0.7279693 28178.79099 0.0000000 0.9527458 9046.727666 0.0000429 0.8263091 29021.87789 0.0000347 0.8122605 9165.408341 0.0000429 0.8263091 30269.89108 0.0000400 0.8122605 9316.869151 0.0000530 0.7982120 31162.97802 0.0000209 0.8122605 9668.960682 0.0036083 0.2567050 42060.31032 0.0013475 0.7420179 9771.571531 0.0148918 0.3269476 44309.89849 0.0003192 0.2081737 10294.7238 0.0000985 0.1864623 44686.33634 0.0054469 0.2924649 10463.66285 0.0003192 0.2145594 45344.38107 0.0000209 0.1724138 10733.07419 0.0000323 0.1724138 46173.68592 0.0000001 0.0740741 12480.3404 0.0002996 0.2005109 47417.69242 0.0000001 0.0881226 12651.6673 F6CSH 0.0000000 0.9801587 10034.41899 0.0021086 0.7203065 13920.46348 0.0000000 0.9801587 10134.16987 0.0003619 0.7624521 14102.40615 0.0000000 0.9801587 10164.38262 0.0178165 0.6717752 15178.11257 0.0000000 0.9801587 10179.25579 0.0029193 0.7279693 15361.4929 0.0000000 0.9801587 10199.786 0.0001571 0.7841635 15915.33173 0.0000000 0.9801587 10247.31042 0.0000920 0.8122605 16093.36476 0.0000000 0.9801587 10308.99693 0.0010102 0.7279693 17426.66866 0.0000000 0.9801587 10324.98028 0.0000042 0.8544061 18048.90648 0.0000000 0.9801587 10351.28255 0.0186220 0.3128991 23037.50948 0.0000000 0.9801587 10382.71287 0.0000013 0.8684547 28178.79099 0.0000000 0.9801587 10437.59021 0.0000000 0.9527458 29021.87789 0.0000000 0.9801587 10456.27805 0.0000000 0.9246488 30269.89108 0.0000000 0.9365079 10586.5933 0.0000039 0.8403576 31162.97802 0.0000000 0.9656085 10639.37916 0.0186220 0.2988506 33490.36172 0.0000000 0.9801587 10659.7841 0.0089131 0.2988506 34479.07332 0.0000000 0.9656085 10714.34361 0.0001376 0.2362708 42060.31032 0.0000000 0.9365079 10736.08599 0.0036083 0.2848020 44686.33634 0.0000002 0.8928571 10828.01891 0.0066526 0.2848020 45344.38107 0.0000001 0.9365079 10910.64978 0.0005243 0.2286079 46173.68592 0.0000004 0.8928571 10929.45696 0.0040055 0.2848020 47417.69242 0.0001155 0.7764550 10952.36983 F6CSH 0.0000000 0.9814815 10034.41899 0.0078125 0.6746032 11084.38324 0.0000000 0.9814815 10134.16987 0.0018417 0.2526455 11471.46464 0.0000000 0.9814815 10164.38262 0.0001518 0.2089947 11571.8247 0.0000000 0.9814815 10179.25579 0.0000000 0.0925926 11623.61757 0.0000000 0.9814815 10199.786 0.0000001 0.0780423 11673.12834 0.0000000 0.9814815 10247.31042 0.0000000 0.0343915 11750.83857 0.0000000 0.9814815 10308.99693 0.0000000 0.0634921 11779.63819 0.0000000 0.9814815 10324.98028 0.0000000 0.0343915 11824.74215 0.0000000 0.9814815 10351.28255 0.0000000 0.0634921 11886.09971 0.0000000 0.9814815 10382.71287 0.0000000 0.0343915 11982.74557 0.0000000 0.9814815 10437.59021 0.0000000 0.0489418 12017.01197 0.0000000 0.9814815 10456.27805 0.0000000 0.0343915 12050.07302 0.0000000 0.9543210 10586.5933 0.0000000 0.0198413 12170.65112 0.0000000 0.9679012 10639.37916 0.0000000 0.0634921 12200.58095 0.0000000 0.9814815 10659.7841 0.0000007 0.1362434 12243.19154 0.0000000 0.9679012 10714.34361 0.0110152 0.6600529 12577.78835 0.0000000 0.9679012 10736.08599 0.0000021 0.1216931 12747.38612 0.0000066 0.8456790 10828.01891 0.0000000 0.0634921 12889.83948 0.0000000 0.9271605 10910.64978 0.0000000 0.0198413 13157.2016 0.0000002 0.9000000 10929.45696 0.0000000 0.0780423 13373.91926 0.0000646 0.7777778 10952.36983 0.0001624 0.2089947 13429.21615 0.0000429 0.8049383 11084.38324 0.0001624 0.2235450 13859.59685 0.0096227 0.6827160 11141.2016 0.0001738 0.2235450 13899.93271 0.0213677 0.6827160 11175.10112 0.0000000 0.0198413 14029.68761 0.0000348 0.1913580 11471.46464 0.0000000 0.0198413 14160.13964 0.0004124 0.2537037 11571.8247 0.0000000 0.0198413 14336.01183 0.0000001 0.0907407 11623.61757 0.0000000 0.0489418 14434.26849 0.0000009 0.1179012 11673.12834 0.0001078 0.8055556 14665.79315 0.0000000 0.0419753 11750.83857 0.0000221 0.8201058 14829.46698 0.0000000 0.0419753 11779.63819 0.0042085 0.2817460 15063.55589 0.0000000 0.0283951 11824.74215 0.0006316 0.2671958 15244.10632 0.0000000 0.0419753 11886.09971 0.0000000 0.0198413 15420.52874 0.0000000 0.0283951 11982.74557 0.0000055 0.1362434 15617.44859 0.0000000 0.0827160 12017.01197 0.0000002 0.8928571 15840.99957 0.0000000 0.0283951 12050.07302 0.0325041 0.6309524 16095.07951 0.0000000 0.0283951 12170.65112 0.0000002 0.9074074 16555.54127 0.0000000 0.0555556 12200.58095 0.0000001 0.9074074 16719.92602 0.0000033 0.1506173 12243.19154 0.0000000 0.0634921 17530.97848 0.0000245 0.8049383 12577.78835 0.0000000 0.0343915 17656.32521 0.0000373 0.1641975 12747.38612 0.0000000 0.9801587 18041.10658 0.0000001 0.1098765 12889.83948 0.0000000 0.9365079 18241.15243 0.0000000 0.0148148 13157.2016 0.0000939 0.7764550 18621.70523 0.0000000 0.0691358 13373.91926 0.0000238 0.1798942 23184.82846 0.0013163 0.2592593 13429.21615 0.0262606 0.3253968 23404.4619 0.0006636 0.2592593 13859.59685 0.0000004 0.0925926 33564.57869 0.0013163 0.2592593 13899.93271 0.0000037 0.1362434 33807.33449 0.0000000 0.0283951 14029.68761 0.0000010 0.1507937 39178.65241 0.0000000 0.0148148 14160.13964 0.0005578 0.2380952 39783.20564 0.0000000 0.0148148 14336.01183 0.0000276 0.1798942 43603.62674 0.0000000 0.0283951 14434.26849 0.0000002 0.1071429 44709.3435 0.0000400 0.8185185 14665.79315 0.0000176 0.1507937 45499.42435 0.0000004 0.8728395 14829.46698 0.0000040 0.1362434 46648.55176 0.0040176 0.3080247 15063.55589 0.0416359 0.3544974 47902.96874 0.0000844 0.1913580 15244.10632 0.0000000 0.0148148 15420.52874 0.0000006 0.1506173 15617.44859 0.0000373 0.8049383 15840.99957 0.0000003 0.9000000 16555.54127 0.0000002 0.9000000 16719.92602 0.0000000 0.0555556 17530.97848 0.0000000 0.0148148 17656.32521 0.0000000 0.9814815 18041.10658 0.0000000 0.9135802 18241.15243 0.0000148 0.8185185 18621.70523 0.0001099 0.2049383 23184.82846 0.0000000 0.0555556 33564.57869 0.0000000 0.0962963 33807.33449 0.0000171 0.1913580 39178.65241 0.0004651 0.2129630 39783.20564 0.0000103 0.1777778 43603.62674 0.0000000 0.0827160 44709.3435 0.0000111 0.1506173 45499.42435 0.0001252 0.2185185 46648.55176

TABLE 23 Biomarkers identified by SELDI technology with a p-value smaller or equal to 0.05 that can discriminate secondary DENV infection from OFI. Grouped according to fraction it was found in. Ct1_2 vs 2DF1_2 Ct1_2 vs 2DHF1_2 p-value roc value m/z average p-value roc value m/z average F1CSL 0.0000000 0.0151515 2625.181487 F1CSL 0.0000000 0.0454545 2625.181487 0.0000000 0.0303030 2667.845783 0.0000000 0.0454545 2667.845783 0.0000000 0.0151515 2741.725854 0.0000000 0.0303030 2741.725854 0.0000000 0.0606061 2856.727191 0.0000001 0.0757576 2856.727191 0.0000000 0.0151515 2872.154934 0.0000000 0.0303030 2872.154934 0.0000000 0.0454545 2921.681011 0.0000004 0.0909091 2921.681011 0.0000000 0.0454545 2990.831829 0.0000000 0.0303030 2990.831829 0.0000000 0.0303030 3043.041845 0.0000002 0.0757576 3043.041845 0.0000001 0.0606061 3071.048466 0.0000000 0.0757576 3175.340726 0.0000000 0.0303030 3145.264181 0.0000000 0.0757576 3209.081505 0.0000000 0.0151515 3175.340726 0.0000000 0.0606061 3262.112346 0.0000000 0.0303030 3209.081505 0.0000000 0.0151515 3280.760876 0.0000000 0.0151515 3262.112346 0.0000001 0.0757576 3307.659966 0.0000000 0.0000000 3280.760876 0.0000052 0.1303030 3358.521584 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0.0652174 45254.93201 0.0000001 0.9186795 10575.10057 0.0000011 0.9025765 61369.70702 0.0000000 0.9508857 10596.28169 F6CSL 0.0000046 0.8403576 2990.018687 0.0000001 0.9186795 10616.84987 0.0000168 0.8122605 3360.866902 0.0000000 0.9347826 10636.82577 0.0000050 0.8263091 4179.401548 0.0000000 0.9347826 10654.23706 0.0000022 0.8544061 4198.411686 0.0000002 0.9025765 10665.17962 0.0000016 0.8684547 4252.48155 0.0000012 0.9025765 10694.26283 0.0000260 0.8122605 4354.864569 0.0000003 0.0974235 11996.5121 0.0000007 0.8544061 4410.72305 0.0000009 0.0974235 12052.53166 0.0000022 0.8544061 6487.935491 0.0000006 0.0974235 12974.23339 0.0000019 0.8684547 6689.164478 0.0000000 0.0330113 13065.70027 0.0000099 0.8263091 8842.966359 0.0000000 0.0330113 13099.66919 0.0000039 0.8544061 9046.727666 0.0000001 0.0652174 13187.45517 0.0029193 0.7139208 9165.408341 0.0000000 0.0169082 13233.79106 0.0001288 0.2081737 10733.07419 0.0000000 0.0330113 13291.70681 0.0000001 0.0881226 12480.3404 0.0000002 0.0813205 13320.77403 0.0000000 0.0600255 12651.6673 0.0000000 0.0491143 13356.70436 F6CSH 0.0000000 0.9801587 10034.41899 0.0000007 0.0974235 13426.83767 0.0000000 0.9801587 10134.16987 0.0000001 0.0813205 14019.70586 0.0000000 0.9801587 10164.38262 0.0000000 0.0169082 14214.24986 0.0000000 0.9801587 10179.25579 0.0000000 0.0169082 14344.33114 0.0000000 0.9801587 10199.786 0.0000000 0.0491143 17653.98849 0.0000000 0.9801587 10247.31042 0.0000001 0.0813205 45254.93201 0.0000000 0.9801587 10308.99693 F6CSL 0.0000001 0.8965517 2990.018687 0.0000000 0.9801587 10324.98028 0.0000016 0.8544061 3360.866902 0.0000000 0.9801587 10351.28255 0.0000002 0.8965517 4410.72305 0.0000000 0.9801587 10382.71287 0.0000000 0.8965517 6487.935491 0.0000000 0.9801587 10437.59021 0.0000000 0.9386973 6689.164478 0.0000000 0.9801587 10456.27805 0.0000000 0.9386973 8842.966359 0.0000000 0.9365079 10586.5933 0.0000000 0.9527458 9046.727666 0.0000000 0.9656085 10639.37916 0.0000001 0.0740741 12480.3404 0.0000000 0.9801587 10659.7841 0.0000001 0.0881226 12651.6673 0.0000000 0.9656085 10714.34361 0.0000000 0.9527458 29021.87789 0.0000000 0.9365079 10736.08599 0.0000000 0.9246488 30269.89108 0.0000001 0.9365079 10910.64978 F6CSH 0.0000000 0.9814815 10034.41899 0.0000000 0.0925926 11623.61757 0.0000000 0.9814815 10134.16987 0.0000001 0.0780423 11673.12834 0.0000000 0.9814815 10164.38262 0.0000000 0.0343915 11750.83857 0.0000000 0.9814815 10179.25579 0.0000000 0.0634921 11779.63819 0.0000000 0.9814815 10199.786 0.0000000 0.0343915 11824.74215 0.0000000 0.9814815 10247.31042 0.0000000 0.0634921 11886.09971 0.0000000 0.9814815 10308.99693 0.0000000 0.0343915 11982.74557 0.0000000 0.9814815 10324.98028 0.0000000 0.0489418 12017.01197 0.0000000 0.9814815 10351.28255 0.0000000 0.0343915 12050.07302 0.0000000 0.9814815 10382.71287 0.0000000 0.0198413 12170.65112 0.0000000 0.9814815 10437.59021 0.0000000 0.0634921 12200.58095 0.0000000 0.9814815 10456.27805 0.0000000 0.0634921 12889.83948 0.0000000 0.9543210 10586.5933 0.0000000 0.0198413 13157.2016 0.0000000 0.9679012 10639.37916 0.0000000 0.0780423 13373.91926 0.0000000 0.9814815 10659.7841 0.0000000 0.0198413 14029.68761 0.0000000 0.9679012 10714.34361 0.0000000 0.0198413 14160.13964 0.0000000 0.9679012 10736.08599 0.0000000 0.0198413 14336.01183 0.0000000 0.9271605 10910.64978 0.0000000 0.0489418 14434.26849 0.0000002 0.9000000 10929.45696 0.0000000 0.0198413 15420.52874 0.0000001 0.0907407 11623.61757 0.0000002 0.9074074 16555.54127 0.0000000 0.0419753 11750.83857 0.0000001 0.9074074 16719.92602 0.0000000 0.0419753 11779.63819 0.0000000 0.0634921 17530.97848 0.0000000 0.0283951 11824.74215 0.0000000 0.0343915 17656.32521 0.0000000 0.0419753 11886.09971 0.0000000 0.9801587 18041.10658 0.0000000 0.0283951 11982.74557 0.0000000 0.9365079 18241.15243 0.0000000 0.0827160 12017.01197 0.0000004 0.0925926 33564.57869 0.0000000 0.0283951 12050.07302 0.0000000 0.0283951 12170.65112 0.0000000 0.0555556 12200.58095 0.0000000 0.0148148 13157.2016 0.0000000 0.0691358 13373.91926 0.0000000 0.0283951 14029.68761 0.0000000 0.0148148 14160.13964 0.0000000 0.0148148 14336.01183 0.0000000 0.0283951 14434.26849 0.0000000 0.0148148 15420.52874 0.0000003 0.9000000 16555.54127 0.0000002 0.9000000 16719.92602 0.0000000 0.0555556 17530.97848 0.0000000 0.0148148 17656.32521 0.0000000 0.9814815 18041.10658 0.0000000 0.9135802 18241.15243 0.0000000 0.0555556 33564.57869 0.0000000 0.0962963 33807.33449 0.0000000 0.0827160 44709.3435

TABLE 24 Biomarkers identified by SELDI technology with a p-value smaller or equal to 0.05 that can discriminate primary DENV from secondary DENV infection. Grouped according to fraction it was found in. 1DF1_2 vs 2DF1_2 1DHF1_2 vs 2DHF1_2 p-value roc value m/z average p-value roc value m/z average 0.0000000 0.9833333 2667.845783 F1CSL 0.0000011 0.9611111 2625.181487 0.0000000 0.9833333 2856.727191 0.0000005 0.9833333 2667.845783 0.0000000 0.9833333 2872.154934 0.0000096 0.9388889 2741.725854 0.0000000 0.9666667 2921.681011 0.0000008 0.9833333 2856.727191 0.0000003 0.9333333 2990.831829 0.0000006 0.9833333 2872.154934 0.0000020 0.9000000 3043.041845 0.0000065 0.9472222 2897.527715 0.0000000 0.9833333 3145.264181 0.0000019 0.9611111 2921.681011 0.0000000 0.9666667 3175.340726 0.0000029 0.9611111 2938.078868 0.0000000 0.9833333 3209.081505 0.0000015 0.9611111 3175.340726 0.0000000 0.9833333 3262.112346 0.0000038 0.9611111 3209.081505 0.0000001 0.9500000 3280.760876 0.0000013 0.9833333 3262.112346 0.0000001 0.9500000 3307.659966 0.0000011 0.9611111 3280.760876 0.0000010 0.9166667 3358.521584 0.0000124 0.9166667 3307.659966 0.0000000 0.9833333 3420.037403 0.0004513 0.8444444 3358.521584 0.0000000 0.9833333 3437.42944 0.0000074 0.9166667 3420.037403 0.0000003 0.9333333 3459.797001 0.0000084 0.9166667 3437.42944 0.0000000 0.9833333 3511.538012 0.0000038 0.9611111 3459.786786 0.0000000 0.9833333 3589.060244 0.0000038 0.9611111 3459.797001 0.0000000 0.9833333 3631.061336 0.0000010 0.9611111 3511.538012 0.0000001 0.9333333 3680.115892 0.0000013 0.9833333 3589.060244 0.0000000 0.9666667 3799.639473 0.0000033 0.9611111 3680.115892 0.0000000 0.9833333 3814.303638 0.0000029 0.9833333 3799.639473 0.0000000 0.9833333 3863.107595 0.0000006 0.9833333 3814.303638 0.0000000 0.9666667 3884.602302 0.0000015 0.9833333 3863.107595 0.0000001 0.9500000 3923.916704 0.0000232 0.9166667 3923.916704 0.0000010 0.9166667 3949.327603 0.0000007 0.9833333 4063.241745 0.0000001 0.9666667 4063.241745 0.0000159 0.9166667 4182.768879 0.0000003 0.9333333 4143.916106 0.0000006 0.9833333 4299.724384 0.0000003 0.9333333 4182.768879 0.0000084 0.9388889 4417.386857 0.0000001 0.9666667 4279.033423 0.0000038 0.9388889 4468.900331 0.0000000 0.9666667 4299.724384 0.0000043 0.9611111 4492.301874 0.0000001 0.9333333 4417.386857 0.0000065 0.9611111 4524.063737 0.0000002 0.9333333 4435.802022 0.0000050 0.9472222 4646.271132 0.0000003 0.9333333 4450.645147 0.0000043 0.0444444 5675.016336 0.0000000 0.9500000 4468.900331 0.0000013 0.0222222 5689.765089 0.0000001 0.9500000 4492.301874 0.0004513 0.1333333 6013.779325 0.0000001 0.9388889 4524.063737 0.0000232 0.0888889 7415.948421 0.0000013 0.9166667 4573.192933 0.0000074 0.0666667 7484.032788 0.0000018 0.9166667 4588.379469 0.0000022 0.0666667 7495.290949 0.0000001 0.9388889 4646.271132 0.0000480 0.9166667 8459.519265 0.0000028 0.1000000 5689.765089 0.0000019 0.0222222 10289.18681 0.0000013 0.0944444 5767.809439 0.0000232 0.0888889 11743.17361 0.0000023 0.1000000 6487.509564 0.0000140 0.0888889 11902.07203 0.0000002 0.0666667 6591.475901 0.0000480 0.0888889 12083.60453 0.0000052 0.1000000 6642.439305 0.0000029 0.0666667 13375.41938 0.0000005 0.0833333 6652.107044 0.0000096 0.0666667 14757.7248 0.0000001 0.0500000 6683.82645 0.0000038 0.0444444 44810.68881 0.0000001 0.0500000 6805.698908 0.0000065 0.0444444 45184.10294 0.0000001 0.0333333 6860.453871 0.0000074 0.0444444 45616.26741 0.0000004 0.0833333 7415.948421 0.0000084 0.0666667 46357.92645 0.0000003 0.0833333 7484.032788 0.0008932 0.2277778 10130.28134 0.0000001 0.0333333 7495.290949 0.0000570 0.1444444 10171.11128 0.0000017 0.1000000 7865.986549 0.0000140 0.1277778 10196.44234 0.0000002 0.0666667 11491.70855 0.0000063 0.1111111 10351.51024 0.0000001 0.0666667 11677.83181 0.0000017 0.0944444 10369.2482 0.0000001 0.0666667 11743.17361 0.0000007 0.0777778 10413.34138 0.0000002 0.0666667 11902.07203 0.0000009 0.0777778 10444.68912 0.0000005 0.0833333 12590.76335 0.0000010 0.0777778 10461.79685 0.0000007 0.0833333 13115.34777 0.0000011 0.0944444 10481.71491 0.0000000 0.0166667 13375.41938 0.0000008 0.0777778 10490.47668 0.0000008 0.0833333 14757.7248 0.0000012 0.0944444 10507.40716 0.0000023 0.0833333 15198.38517 0.0000003 0.9333333 10755.48072 0.0000047 0.1000000 22937.26034 0.0000001 0.9555556 10783.37546 0.0000012 0.0666667 23544.76704 0.0000001 0.9555556 10808.95097 0.0000011 0.9222222 35405.0346 0.0000001 0.9666667 10835.54836 0.0000001 0.0333333 44810.68881 0.0000001 0.9555556 10882.15141 0.0000001 0.0333333 45184.10294 0.0000000 0.9833333 10908.0378 0.0000001 0.0333333 45616.26741 0.0000000 0.9833333 10926.28045 0.0000001 0.0333333 46357.92645 0.0000000 0.9833333 10951.30804 0.0270506 0.3138889 10130.28134 0.0000007 0.0833333 11383.78669 0.0020619 0.2625000 10171.11128 0.0000000 0.9666667 11823.52687 0.0016282 0.2666667 10196.44234 0.0000001 0.9666667 11853.93279 0.0003589 0.2250000 10241.85866 0.0000000 0.9833333 12009.62084 0.0000001 0.0861111 10490.47668 0.0000000 0.9833333 12080.71023 0.0000001 0.0722222 10507.40716 0.0000000 0.9833333 12112.68049 0.0000002 0.1000000 10514.61735 0.0000000 0.9833333 12132.64107 0.0000001 0.9166667 10808.95097 0.0000000 0.9500000 12163.0594 0.0000000 0.9444444 10835.54836 0.0000000 0.9833333 12268.93847 0.0000001 0.9305556 10882.15141 0.0000001 0.9500000 12358.50727 0.0000001 0.9027778 10908.0378 0.0000000 0.9833333 12493.48295 0.0000000 0.9305556 10926.28045 0.0000000 0.0333333 12954.81113 0.0000000 0.9861111 10951.30804 0.0000013 0.1000000 13063.59409 0.0000005 0.9027778 11078.55501 0.0000001 0.0500000 13086.15535 0.0000003 0.9027778 11725.21956 0.0000000 0.9833333 13930.38141 0.0000002 0.9027778 11823.52687 0.0000003 0.9333333 14140.38344 0.0000000 0.9305556 11853.93279 0.0000001 0.0333333 14449.85539 0.0000000 0.9722222 12009.62084 0.0000002 0.0666667 14639.78045 0.0000000 0.9861111 12080.71023 0.0000000 0.0166667 14934.23152 0.0000000 0.9583333 12112.68049 0.0000002 0.9333333 100409.516 0.0000000 0.9861111 12132.64107 0.0000018 0.9166667 101137.8334 0.0000000 0.9861111 12163.0594 0.0000002 0.9500000 102198.3507 0.0000000 0.9861111 12268.93847 0.0000011 0.9166667 107068.7099 0.0000000 0.9861111 12358.50727 0.0000002 0.9500000 113333.4714 0.0000000 0.9861111 12493.48295 0.0000004 0.9333333 174369.2245 0.0000001 0.9069444 12562.71997 0.0000000 0.9666667 192541.7062 0.0000000 0.9583333 13930.38141 F1ISL 0.0000003 0.0400000 2503.154582 0.0000000 0.9583333 14140.38344 0.0000010 0.0600000 2513.073032 0.0000006 0.9027778 192541.7062 0.0000053 0.1000000 2517.656451 F1ISL 0.0000000 0.0166667 2503.154582 0.0000060 0.0800000 2522.859861 0.0000005 0.1000000 2534.62918 0.0000012 0.0600000 2523.669165 0.0000000 0.0722222 2565.111611 0.0000007 0.0600000 2524.520651 0.0000001 0.0861111 2595.854473 0.0000030 0.0800000 2549.242272 0.0000000 0.0583333 2601.511801 0.0000067 0.1000000 2565.111611 0.0000002 0.0861111 2614.032211 0.0000008 0.0600000 2570.087202 0.0000000 0.0444444 2617.583867 0.0000021 0.0800000 2595.854473 0.0000004 0.1000000 2621.696749 0.0000053 0.1000000 2601.511801 0.0000001 0.0722222 2634.20847 0.0000005 0.0400000 2614.032211 0.0000001 0.0861111 2635.700585 0.0000006 0.0400000 2617.583867 0.0000000 0.0166667 2637.169955 0.0000013 0.0600000 2621.696749 0.0000000 0.0444444 2638.060903 0.0000024 0.0800000 2634.20847 0.0000000 0.0444444 2639.173428 0.0000005 0.0400000 2635.700585 0.0000000 0.0166667 2641.926515 0.0000003 0.0400000 2637.169955 0.0000000 0.0166667 2642.885878 0.0000003 0.0400000 2637.618569 0.0000000 0.0444444 2643.949918 0.0000004 0.0400000 2638.060903 0.0000000 0.0583333 2658.967508 0.0000004 0.0400000 2638.54484 0.0000000 0.0444444 2659.814331 0.0000001 0.0200000 2639.173428 0.0000000 0.0166667 2661.84737 0.0000001 0.0200000 2639.795493 0.0000000 0.0166667 2662.858635 0.0000001 0.0200000 2640.467576 0.0000000 0.0305556 2666.570765 0.0000001 0.0200000 2641.164374 0.0000000 0.0861111 2674.971433 0.0000001 0.0200000 2641.926515 0.0000000 0.0583333 2679.146726 0.0000001 0.0200000 2642.885878 0.0000000 0.0166667 2681.308781 0.0000030 0.0600000 2643.949918 0.0000000 0.0166667 2684.692433 0.0000053 0.1000000 2646.227988 0.0000000 0.0166667 2686.45453 0.0000005 0.0400000 2657.390501 0.0000000 0.0166667 2704.270371 0.0000105 0.1000000 2658.351659 0.0000000 0.0305556 2709.71622 0.0000013 0.0600000 2658.967508 0.0000000 0.0583333 2726.112884 0.0000010 0.0600000 2659.814331 0.0000004 0.1000000 2738.579429 0.0000053 0.0800000 2661.194232 0.0000001 0.0861111 2749.363653 0.0000007 0.0600000 2661.84737 0.0000000 0.0166667 2752.680443 0.0000015 0.0800000 2662.858635 0.0000000 0.0166667 2753.648132 0.0000009 0.0600000 2666.570765 0.0000000 0.0722222 2772.763638 0.0000021 0.0600000 2679.146726 0.0000000 0.0305556 2775.410216 0.0000001 0.0200000 2681.308781 0.0000001 0.0722222 2796.034541 0.0000021 0.0600000 2684.692433 0.0000002 0.0861111 2803.236487 0.0000003 0.0600000 2686.45453 0.0000000 0.0166667 2811.436125 0.0000002 0.0400000 2704.270371 0.0000000 0.0583333 2817.188037 0.0000004 0.0600000 2709.71622 0.0000000 0.0166667 2824.989455 0.0000131 0.1000000 2738.579429 0.0000010 0.1000000 2866.751136 0.0000002 0.0400000 2749.363653 0.0000000 0.0305556 2879.703192 0.0000053 0.0800000 2752.680443 0.0000000 0.0166667 2883.890105 0.0000182 0.1000000 2753.648132 0.0000000 0.0166667 2887.453423 0.0000182 0.1000000 2753.857675 0.0000000 0.0583333 2894.062647 0.0000182 0.1000000 2753.946851 0.0000000 0.0583333 2904.031336 0.0000005 0.0400000 2755.192724 0.0000000 0.0444444 2906.91955 0.0000008 0.0600000 2772.763638 0.0000000 0.0583333 2908.899563 0.0000021 0.0800000 2788.581536 0.0000000 0.0166667 2911.83993 0.0000030 0.0800000 2791.185756 0.0000001 0.0861111 2915.763212 0.0000024 0.0800000 2796.034541 0.0000001 0.0722222 2930.543416 0.0000024 0.0800000 2803.236487 0.0000000 0.0583333 2933.649099 0.0000003 0.0400000 2811.436125 0.0000000 0.0583333 2948.917414 0.0000038 0.0600000 2817.188037 0.0000000 0.0305556 2951.608271 0.0000001 0.0200000 2819.017882 0.0000000 0.0166667 2953.403838 0.0000021 0.0600000 2824.989455 0.0000000 0.0166667 2956.963771 0.0000012 0.0600000 2883.890105 0.0000000 0.0444444 2976.912269 0.0000034 0.0800000 2887.453423 0.0000000 0.0444444 2993.494244 0.0000002 0.0200000 2933.649099 0.0000000 0.0166667 3015.428516 0.0000012 0.0600000 2948.917414 0.0000000 0.0444444 3020.653111 0.0000005 0.0400000 2951.608271 0.0000003 0.0861111 3042.639369 0.0000053 0.0800000 2953.403838 0.0000000 0.0583333 3066.155713 0.0000024 0.0800000 2976.912269 0.0000000 0.0583333 3083.362119 0.0000048 0.0800000 2993.494244 0.0000000 0.0583333 3092.389989 0.0000003 0.0400000 2997.743006 0.0000000 0.0444444 3113.412549 0.0000146 0.1000000 3015.428516 0.0000001 0.1000000 3149.522222 0.0000034 0.1000000 3066.155713 0.0000000 0.0305556 3158.254319 0.0000006 0.0800000 3092.389989 0.0000000 0.0305556 3177.822062 0.0000005 0.0600000 3129.459825 0.0000001 0.0861111 3213.068347 0.0000010 0.0800000 3158.254319 0.0000000 0.0305556 3221.413524 0.0000043 0.1000000 3197.777988 0.0000004 0.1000000 3235.296597 0.0000053 0.1000000 3213.068347 0.0000003 0.1000000 3314.079782 0.0000060 0.1000000 3221.413524 0.0000000 0.0444444 3318.297755 0.0000034 0.1000000 3235.296597 0.0000000 0.0722222 3324.2993 0.0000009 0.0600000 3276.379562 0.0000001 0.1000000 3397.259967 0.0000251 0.1000000 3285.606735 0.0000000 0.0444444 3468.46665 0.0000060 0.0800000 3290.515874 0.0000000 0.0444444 3515.357908 0.0000006 0.0600000 3315.115414 0.0000000 0.0444444 3528.345658 0.0000015 0.0600000 3315.909084 0.0000003 0.0861111 3536.763944 0.0000024 0.0800000 3316.53443 0.0000002 0.1000000 3588.453952 0.0000001 0.0200000 3318.297755 0.0000003 0.1000000 3604.347348 0.0000251 0.1000000 3321.260411 0.0000000 0.0583333 3644.486854 0.0000038 0.0800000 3324.2993 0.0000000 0.0583333 3687.780782 0.0000048 0.1000000 3369.373189 0.0000000 0.0583333 3699.672565 0.0000013 0.0600000 3397.259967 0.0000009 0.1000000 3739.751205 0.0000004 0.0400000 3445.720567 0.0000000 0.0305556 3772.107649 0.0000004 0.0600000 3468.46665 0.0000000 0.0305556 3827.712827 0.0000004 0.0600000 3515.357908 0.0000000 0.0444444 3891.091076 0.0000118 0.1000000 3528.345658 0.0000003 0.1000000 48592.66902 0.0000038 0.1000000 3536.763944 F1ISH 0.0000000 0.0129630 10011.99352 0.0000008 0.0600000 3644.486854 0.0000000 0.0129630 10020.52117 0.0000021 0.0800000 3670.116515 0.0000000 0.0129630 10025.43594 0.0000021 0.0600000 3687.780782 0.0000000 0.0129630 10028.64467 0.0000251 0.1000000 3699.672565 0.0000000 0.0129630 10036.97216 0.0000038 0.0800000 3772.107649 0.0000000 0.0129630 10051.63047 0.0000009 0.0600000 3972.21957 0.0000000 0.0129630 10061.98167 0.0000311 0.1000000 4301.196114 0.0000000 0.0129630 10072.44679 F1ISH 0.0000008 0.0154321 10011.99352 0.0000000 0.0129630 10081.58862 0.0000008 0.0154321 10020.52117 0.0000000 0.0129630 10090.2363 0.0000008 0.0154321 10025.43594 0.0000000 0.0129630 10098.45601 0.0000008 0.0154321 10028.64467 0.0000000 0.0129630 10105.92173 0.0000008 0.0154321 10036.97216 0.0000000 0.0129630 10114.78187 0.0000008 0.0154321 10051.63047 0.0000000 0.0129630 10135.6053 0.0000008 0.0154321 10061.98167 0.0000000 0.0129630 10147.36272 0.0000008 0.0154321 10072.44679 0.0000000 0.0296296 10162.00816 0.0000008 0.0154321 10081.58862 0.0000000 0.0296296 10175.58746 0.0000008 0.0154321 10090.2363 0.0000000 0.0296296 10187.91964 0.0000010 0.0154321 10098.45601 0.0000000 0.0462963 10198.46953 0.0000015 0.0154321 10105.92173 0.0000000 0.0462963 10208.17418 0.0000010 0.0154321 10114.78187 0.0000000 0.0462963 10217.54983 0.0000010 0.0154321 10135.6053 0.0000000 0.0462963 10230.57425 0.0000021 0.0154321 10147.36272 0.0000004 0.0629630 10239.94928 0.0000018 0.0154321 10162.00816 0.0000002 0.0796296 10262.82559 0.0000032 0.0370370 10175.58746 0.0000001 0.0462963 10294.56991 0.0000077 0.0586420 10187.91964 0.0000000 0.0462963 10306.64326 0.0000118 0.0802469 10198.46953 0.0000001 0.0462963 10324.32336 0.0000067 0.0586420 10208.17418 0.0000002 0.0629630 10346.11123 0.0000089 0.0586420 10217.54983 0.0000001 0.0629630 10363.09118 0.0000399 0.0802469 10521.88611 0.0000001 0.0462963 10383.28509 0.0000350 0.0586420 11087.90879 0.0000002 0.0629630 10402.18844 0.0000021 0.0370370 11105.68287 0.0000007 0.0796296 10408.85968 0.0000118 0.0586420 11118.40053 0.0000003 0.0629630 10414.59628 0.0000089 0.9475309 11508.64295 0.0000007 0.0962963 10425.2516 0.0000067 0.9521605 11533.00585 0.0000076 0.0962963 10434.47733 0.0000021 0.9691358 11553.43803 0.0000025 0.0962963 10521.88611 0.0000037 0.9691358 11576.23632 0.0000007 0.0796296 10890.43701 0.0000118 0.9475309 11596.70327 0.0000056 0.0962963 10909.77131 0.0000032 0.9475309 11607.15919 0.0000008 0.0796296 10994.43525 0.0000015 0.9907407 11623.79327 0.0000004 0.0629630 11014.94996 0.0000011 0.9907407 11642.6909 0.0000001 0.0462963 11035.719 0.0000011 0.9907407 11662.86838 0.0000001 0.0462963 11046.66769 0.0000018 0.9907407 11682.71261 0.0000002 0.0796296 11057.41916 0.0000455 0.9043210 11810.11077 0.0000000 0.0462963 11071.04109 0.0000028 0.9691358 11849.00542 0.0000000 0.0129630 11087.90879 0.0000032 0.9691358 11881.99843 0.0000000 0.0296296 11105.68287 0.0000032 0.9691358 11909.00114 0.0000000 0.0296296 11118.40053 0.0000032 0.9691358 11918.54028 0.0000002 0.0462963 11123.52137 0.0000043 0.9691358 11925.28834 0.0000000 0.0296296 11130.42377 0.0000178 0.9259259 11930.35684 0.0000003 0.0796296 11147.3563 0.0000155 0.9043210 11946.65943 0.0000003 0.0796296 11163.91984 0.0000178 0.9089506 12009.23838 0.0000001 0.0462963 11178.66084 0.0000204 0.9259259 12043.45183 0.0000001 0.0629630 11191.97145 0.0000018 0.9907407 12063.7875 0.0000003 0.0962963 11212.36119 0.0000067 0.9475309 12080.17934 0.0000000 0.0462963 11226.91495 0.0000102 0.0586420 12526.96894 0.0000016 0.0962963 11244.4887 0.0000037 0.0370370 12586.1786 0.0000000 0.9666667 11533.00585 0.0000204 0.0802469 12616.77768 0.0000000 0.9500000 11553.43803 0.0000008 0.9907407 13517.3522 0.0000005 0.9231481 11576.23632 0.0000050 0.9475309 13572.89265 0.0000001 0.9500000 11596.70327 0.0000178 0.9043210 13664.61109 0.0000002 0.9333333 11607.15919 0.0000018 0.9691358 13743.15877 0.0000001 0.9333333 11623.79327 0.0000234 0.9259259 14868.45486 0.0000000 0.9666667 11642.6909 0.0000008 0.9907407 15277.38043 0.0000000 0.9666667 11662.86838 0.0000268 0.9259259 23238.98573 0.0000000 0.9500000 11682.71261 0.0000043 0.9475309 23638.15252 0.0000004 0.9166667 11706.67327 F5CSL 0.0000009 0.9176245 3439.012926 0.0000004 0.9166667 11737.66458 0.0302687 0.3103448 6466.54012 0.0000002 0.9166667 11758.16873 0.0003051 0.1724138 6490.498273 0.0000004 0.9166667 11881.99843 0.0000427 0.1551724 6661.944 0.0000004 0.9166667 11909.00114 0.0000009 0.1034483 6689.07332 0.0000004 0.9231481 11925.28834 0.0003319 0.2068966 6863.56499 0.0000003 0.9231481 11930.35684 0.0036071 0.2586207 33593.85227 0.0000002 0.9333333 12043.45183 0.0050912 0.2758621 34007.46796 0.0000001 0.9333333 12063.7875 0.0000037 0.0706522 10146.3839 0.0000002 0.9333333 12080.17934 0.0000074 0.0706522 10162.43733 0.0000002 0.9333333 12095.12377 0.0000064 0.0706522 10178.08813 0.0000003 0.9166667 12101.64446 0.0000306 0.0896739 10215.263 0.0000000 0.9833333 13517.3522 0.0000125 0.0896739 10254.36842 0.0000002 0.9333333 13572.89265 0.0000006 0.0135870 10281.47372 0.0000000 1.0000000 13743.15877 0.0000005 0.0326087 10296.18987 0.0000007 0.9166667 14868.45486 0.0000012 0.0706522 10308.69901 0.0000000 0.9666667 15277.38043 0.0000006 0.0326087 10341.43948 0.0000001 0.9666667 75269.22784 0.0000006 0.0326087 10359.22541 0.0000002 0.9333333 150365.8563 0.0000005 0.0135870 10373.50565 F5CSL 0.0000006 0.8979885 3439.012926 0.0000010 0.0516304 10388.7689 0.0000627 0.1896552 6490.498273 0.0000021 0.0516304 10402.74887 0.0000499 0.1896552 6661.944 0.0000043 0.0516304 10418.95812 0.0000000 0.0603448 6689.07332 0.0000096 0.0896739 10433.30772 0.0000102 0.1321839 6863.56499 0.0000185 0.0896739 10452.21013 0.0000034 0.1321839 25619.17312 0.0000096 0.0706522 10465.90587 0.0062592 0.3045977 34736.3154 0.0000125 0.0896739 10481.18415 0.0000009 0.0731225 10281.47372 0.0000270 0.0896739 10498.6017 0.0000007 0.0731225 10296.18987 0.0000096 0.0706522 10906.58841 0.0000007 0.0909091 10308.69901 0.0000162 0.0896739 10927.60682 0.0000005 0.0909091 10341.43948 0.0000008 0.9483696 12052.53166 0.0000001 0.0553360 10359.22541 0.0000110 0.9144022 14019.70586 0.0000001 0.0553360 10373.50565 0.0000010 0.9524457 14214.24986 0.0000007 0.0909091 10388.7689 0.0000005 0.9673913 14344.33114 0.0000003 0.0731225 10402.74887 0.0000001 0.9864130 17653.98849 0.0000002 0.0731225 10418.95812 0.0000005 0.9483696 17809.61765 0.0000002 0.0731225 10433.30772 0.0000059 0.0926724 3360.866902 0.0000001 0.0553360 10452.21013 0.0000115 0.0926724 4198.411686 0.0000001 0.0731225 10465.90587 0.0104430 0.7327586 5014.908804 0.0000002 0.0553360 10481.18415 0.0166282 0.6939655 5017.339281 0.0000005 0.0731225 10508.92986 0.0051433 0.2284483 6487.935491 0.0000011 0.0909091 10575.10057 0.0000410 0.1508621 6689.164478 0.0000006 0.9268775 12052.53166 0.0007600 0.7909483 10733.07419 0.0000040 0.8913043 13356.70436 0.0000679 0.1314655 12910.82457 0.0000000 0.9802372 14019.70586 0.0000042 0.0926724 17426.66866 0.0000000 0.9624506 14214.24986 0.0000074 0.1120690 18048.90648 0.0000000 0.9802372 14344.33114 0.0000093 0.0926724 30269.89108 0.0000001 0.9446640 15566.82603 0.0000038 0.0926724 31162.97802 0.0000002 0.9446640 15645.08235 0.0000016 0.0357143 10134.16987 0.0000002 0.9446640 17653.98849 0.0000003 0.0357143 10164.38262 0.0000002 0.9446640 17809.61765 0.0000005 0.0357143 10179.25579 0.0000145 0.8557312 79145.35138 0.0000006 0.0357143 10199.786 F6CSL 0.0000007 0.1222571 2990.018687 0.0000084 0.0765306 10247.31042 0.0000001 0.0595611 3360.866902 0.0000005 0.0357143 10308.99693 0.0000009 0.1065831 4252.48155 0.0000003 0.0357143 10324.98028 0.0000050 0.1379310 6487.935491 0.0000003 0.0357143 10351.28255 0.0008161 0.2319749 6544.667343 0.0000003 0.0357143 10382.71287 0.0000000 0.0438871 6689.164478 0.0000003 0.0357143 10437.59021 0.0000016 0.0909091 6855.504433 0.0000003 0.0357143 10456.27805 0.0000066 0.1379310 6871.100138 0.0000084 0.0969388 10586.5933 0.0000001 0.0752351 12910.82457 0.0000005 0.0153061 10639.37916 0.0000003 0.0752351 13920.46348 0.0000002 0.0153061 10659.7841 0.0000003 0.0909091 14102.40615 0.0000008 0.0357143 10714.34361 0.0000004 0.0909091 17426.66866 0.0000095 0.0969388 10736.08599 0.0000008 0.0909091 18048.90648 0.0000003 0.9795918 11623.61757 0.0000005 0.1222571 28178.79099 0.0000005 0.9795918 11673.12834 0.0000000 0.0595611 29021.87789 0.0000002 0.9795918 11750.83857 0.0000000 0.0595611 30269.89108 0.0000010 0.9591837 11779.63819 0.0000006 0.0909091 31162.97802 0.0000003 0.9795918 11824.74215 F6CSH 0.0000000 0.0388889 10034.41899 0.0000003 0.9795918 11886.09971 0.0000000 0.0071429 10134.16987 0.0000002 0.9795918 11982.74557 0.0000000 0.0071429 10164.38262 0.0000002 0.9795918 12017.01197 0.0000000 0.0071429 10179.25579 0.0000002 0.9795918 12050.07302 0.0000000 0.0071429 10199.786 0.0000002 0.9795918 12170.65112 0.0000000 0.0230159 10247.31042 0.0000002 0.9795918 12200.58095 0.0000000 0.0071429 10308.99693 0.0000002 0.9795918 12243.19154 0.0000000 0.0071429 10324.98028 0.0000003 0.9795918 13157.2016 0.0000000 0.0071429 10351.28255 0.0000095 0.9183673 13373.91926 0.0000000 0.0071429 10382.71287 0.0000002 0.9795918 14029.68761 0.0000000 0.0071429 10437.59021 0.0000002 0.9795918 14160.13964 0.0000000 0.0071429 10456.27805 0.0000002 0.9795918 14336.01183 0.0000000 0.0230159 10639.37916 0.0000005 0.9795918 14434.26849 0.0000000 0.0230159 10659.7841 0.0000005 0.0357143 14665.79315 0.0000000 0.0230159 10714.34361 0.0000030 0.9591837 15420.52874 0.0000000 0.0230159 10736.08599 0.0000002 0.9795918 17530.97848 0.0000000 0.0388889 10910.64978 0.0000002 0.9795918 17656.32521 0.0000001 0.0706349 10929.45696 0.0000002 0.0153061 18041.10658 0.0000001 0.9523810 11824.74215 0.0000121 0.0765306 18241.15243 0.0000000 0.9523810 11982.74557 0.0000006 0.9591837 23184.82846 0.0000004 0.9047619 12017.01197 0.0000005 0.9591837 39178.65241 0.0000000 0.9841270 12050.07302 0.0000007 0.9591837 39783.20564 0.0000000 0.9365079 12170.65112 0.0000000 0.9841270 13157.2016 0.0000000 0.9682540 13373.91926 0.0000002 0.9047619 13859.59685 0.0000000 1.0000000 14029.68761 0.0000000 1.0000000 14160.13964 0.0000000 1.0000000 14336.01183 0.0000000 0.9841270 14434.26849 0.0000000 0.9523810 15244.10632 0.0000000 1.0000000 15420.52874 0.0000000 0.9841270 15617.44859 0.0000007 0.9047619 16254.77706 0.0000001 0.9365079 16335.46786 0.0000000 0.9841270 17530.97848 0.0000000 1.0000000 17656.32521 0.0000000 0.0230159 18041.10658 0.0000003 0.9047619 39178.65241 0.0000001 0.9365079 39783.20564

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Each recited range includes all combinations and sub-combinations of ranges, as well as specific numerals contained therein.

All publications and patent applications cited in this specification are herein incorporated by reference in their entirety for all purposes as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference for all purposes.

Although the foregoing invention has been described in detail by way of example for purposes of clarity of understanding, it will be apparent to the artisan that certain changes and modifications are comprehended by the disclosure and can be practiced without undue experimentation within the scope of the appended claims, which are presented by way of illustration not limitation. 

1. A method for qualifying dengue status in a subject comprising: (a) measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24; and (b) correlating the measurement with dengue status.
 2. (canceled)
 3. The method of claim 1, wherein the at least one biomarker is selected from the group consisting of biomarkers of molecular masses of about 2.5, 2.6, 2.7, 2.8, 3.0, 3.2, 3.4, 3.5, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 5.0, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.0, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.4, 7.5, 7.6, 8.2, 8.8, 9.0, 9.3, 9.5, 9.6, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.3, 11.5, 11.7, 11.8, 11.9, 12.1, 12.2, 12.4, 12.5, 12.6, 12.7, 12.9, 13.0, 13.1, 13.2, 13.3, 13.4, 13.5, 13.8, 14.0, 14.1, 14.2, 14.3, 14.4, 15.3, 17.4, 17.7, 17.8, 18.0, 21.5, 22.4, 23.1, 23.3, 23.6, 23.7, 23.8, 25.4, 25.6, 26.5, 26.7, 28.2, 28.4, 29.0, 30.3, 30.8, 31.2, 32.3, 33.5, 33.6, 34.0, 34.2, 34.5, 34.7, 36.5, 36.6, 38.7, 39.5, 39.9, 42.3, 43.5, 44.0, 44.6, 44.7, 45.0, 45.4, 45.5, 45.6, 46.2, 46.6, 46.7, 49.7, 50.5, 51.7, 52.4, 52.6, 53.4, 53.6, 54.3, 54.4, 54.6, 54.8, 55.1, 55.3, 55.8, 56.6, 59.4, 59.5, 61.4, 63.1, 66.6, 66.7, 66.8, 67.1, 69.0, 70.9, 71.3, 71.5, 75.1, 75.2, 75.3, 77.1, 79.1, 79.3, 88.3, 95.7, 109.0, 111.3, 117.2, 123.0, 125.4, 133.4, 133.7, 150.1, 164.6, 188.6, 194.2, and 198.3 kDa. 4-28. (canceled)
 29. The method of claim 1, wherein the correlating is performed by a software classification algorithm.
 30. The method of claim 1, wherein dengue status is selected from chronic symptomatic, chronic asymptomatic, acute, and uninfected.
 31. The method of claim 1, wherein dengue status is selected from dengue versus non-dengue.
 32. The method of claim 1, wherein dengue status is selected from dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS).
 33. The method of claim 1, wherein dengue status is selected from primary dengue infection and secondary dengue infection.
 34. The method of claim 1, further comprising: (c) managing subject treatment based on the status. 35-37. (canceled)
 38. The method of claim 30, wherein, if the measurement correlates with dengue, then managing subject treatment comprises administering one or more drugs selected from the group consisting of paracetamol and antipyretics.
 39. The method of claim 30, further comprising: (d) measuring the at least one biomarker after subject management and correlating the measurement with disease progression. 40-41. (canceled)
 42. A method for determining the course of dengue comprising: (a) measuring, at a first time, at least one biomarker in a biological sample from the subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24; (b) measuring, at a second time, the at least one biomarker in a biological sample from the subject; and (c) comparing the first measurement and the second measurement; wherein the comparative measurements determine the course of dengue. 43-45. (canceled)
 46. The method of claim 42, wherein the at least one biomarker is selected from the group of biomarkers of molecular masses of about 2.5, 2.6, 2.7, 2.8, 3.0, 3.2, 3.4, 3.5, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 5.0, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.0, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.4, 7.5, 7.6, 8.2, 8.8, 9.0, 9.3, 9.5, 9.6, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.3, 11.5, 11.7, 11.8, 11.9, 12.1, 12.2, 12.4, 12.5, 12.6, 12.7, 12.9, 13.0, 13.1, 13.2, 13.3, 13.4, 13.5, 13.8, 14.0, 14.1, 14.2, 14.3, 14.4, 15.3, 17.4, 17.7, 17.8, 18.0, 21.5, 22.4, 23.1, 23.3, 23.6, 23.7, 23.8, 25.4, 25.6, 26.5, 26.7, 28.2, 28.4, 29.0, 30.3, 30.8, 31.2, 32.3, 33.5, 33.6, 34.0, 34.2, 34.5, 34.7, 36.5, 36.6, 38.7, 39.5, 39.9, 42.3, 43.5, 44.0, 44.6, 44.7, 45.0, 45.4, 45.5, 45.6, 46.2, 46.6, 46.7, 49.7, 50.5, 51.7, 52.4, 52.6, 53.4, 53.6, 54.3, 54.4, 54.6, 54.8, 55.1, 55.3, 55.8, 56.6, 59.4, 59.5, 61.4, 63.1, 66.6, 66.7, 66.8, 67.1, 69.0, 70.9, 71.3, 71.5, 75.1, 75.2, 75.3, 77.1, 79.1, 79.3, 88.3, 95.7, 109.0, 111.3, 117.2, 123.0, 125.4, 133.4, 133.7, 150.1, 164.6, 188.6, 194.2, and 198.3 kDa. 47-51. (canceled)
 52. A kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds at least one biomarker from a first group consisting of the Biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24; and (b) instructions for using the solid support to detect a biomarker of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, or Table
 24. 53. The kit of claim 52 comprising instructions for using the solid support to detect at least one biomarker of molecular mass of about 2.5, 2.6, 2.7, 2.8, 3.0, 3.2, 3.4, 3.5, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 5.0, 5.1, 5.2, 5.3, 5.5, 5.6, 5.7, 6.0, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.4, 7.5, 7.6, 8.2, 8.8, 9.0, 9.3, 9.5, 9.6, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.3, 11.5, 11.7, 11.8, 11.9, 12.1, 12.2, 12.4, 12.5, 12.6, 12.7, 12.9, 13.0, 13.1, 13.2, 13.3, 13.4, 13.5, 13.8, 14.0, 14.1, 14.2, 14.3, 14.4, 15.3, 17.4, 17.7, 17.8, 18.0, 21.5, 22.4, 23.1, 23.3, 23.6, 23.7, 23.8, 25.4, 25.6, 26.5, 26.7, 28.2, 28.4, 29.0, 30.3, 30.8, 31.2, 32.3, 33.5, 33.6, 34.0, 34.2, 34.5, 34.7, 36.5, 36.6, 38.7, 39.5, 39.9, 42.3, 43.5, 44.0, 44.6, 44.7, 45.0, 45.4, 45.5, 45.6, 46.2, 46.6, 46.7, 49.7, 50.5, 51.7, 52.4, 52.6, 53.4, 53.6, 54.3, 54.4, 54.6, 54.8, 55.1, 55.3, 55.8, 56.6, 59.4, 59.5, 61.4, 63.1, 66.6, 66.7, 66.8, 67.1, 69.0, 70.9, 71.3, 71.5, 75.1, 75.2, 75.3, 77.1, 79.1, 79.3, 88.3, 95.7, 109.0, 111.3, 117.2, 123.0, 125.4, 133.4, 133.7, 150.1, 164.6, 188.6, 194.2, and 198.3 kDa. 54-55. (canceled)
 56. The kit of claim 52, wherein the solid support comprising a capture reagent is a SELDI probe or a cation exchange adsorbent.
 57. (canceled)
 58. The kit of claim 56, wherein the adsorbent is a metal chelate adsorbent.
 59. The kit of claim 52, additionally comprising: (c) a container containing at least one of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, or Table
 24. 60-68. (canceled)
 69. A software product comprising: (a) code that accesses data attributed to a sample, the data comprising measurement of at least one biomarker in the sample, the biomarker selected from the group consisting of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, and Table 24; and (b) code that executes a classification algorithm that classifies the disease status of the sample as a function of the measurement. 70-76. (canceled)
 77. A method for qualifying dengue status in a subject in comparison to the status of a different infection, the method comprising: (a) measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker specifically indicates the presence of dengue and does not indicate the presence of a different viral infection, wherein the at least one biomarker is selected from the group of the biomarkers of Table 1, Table 2, Table 3, Table 4, Table 5, Table 17, Table 21, or Table 24; and (b) correlating the measuring with dengue status in comparison to the status of the different viral infection.
 78. (canceled)
 79. The method of claim 77, wherein said viral infection comprises another febrile illness. 